Zero Net Energy Assessment & Verification for the West Village Development

Transcription

1 Zero Net Energy Assessment & Verification for the West Village Development Interim Report ET Project Number: Project Manager: Peter Turnbull Pacific Gas and Electric Company Anna LaRue Resource Refocus LLC Prepared By: Greg Risko, P.E. Katie Gustafson NORESCO 2540 Frontier Ave, Suite 100 Boulder, CO Issued: December 29, 2014

2 Copyright, 2014, Pacific Gas and Electric Company. All rights reserved.

3 ACKNOWLEDGEMENTS Pacific Gas and Electric Company s Emerging Technologies Program is responsible for this project. It was developed as part of Pacific Gas and Electric Company s Emerging Technology Technology Assessments Program under internal project number. NORESCO conducted this technology evaluation for Pacific Gas and Electric Company with overall guidance and management from Peter Turnbull. For more information on this project, contact Peter Turnbull at pwt1@pge.com. Peter Turnbull (PG&E) and Anna LaRue (Resource Refocus LLC) provided general oversight. Project guidance was also provided by Mananya Chansanchai (PG&E) and Dr. Carrie Brown (Resource Refocus LLC). The Noresco team coordinated closely with Christine Hammer (Sustainable Design + Behavior) on the aspects of the study that relate to resident behavior. Noresco would like to acknowledge and appreciate the assistance, participation, and leadership of the Carmel Partners West Village team including, but not limited to: Stephanie Martling, Erika Perez, JD McLeod, Forest Gouge, and Sean Blaevoet. LEGAL NOTICE This report was prepared for Pacific Gas and Electric Company for use by its employees and agents. Neither Pacific Gas and Electric Company nor any of its employees and agents: (1) makes any written or oral warranty, expressed or implied, including, but not limited to those concerning merchantability or fitness for a particular purpose; (2) assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, process, method, or policy contained herein; or (3) represents that its use would not infringe any privately owned rights, including, but not limited to, patents, trademarks, or copyrights. i

4 ABBREVIATIONS AND ACRONYMS AFCI CT GFCI EUI HVAC NEM PG&E PV ZNE DHW EUI OAT HPWH ITS WCEC Arc Fault Circuit Interrupter Current Transformer Ground Fault Circuit Interrupter Energy Use Intensity Heating, Ventilation, and Air Conditioning Net Energy Metering A special billing arrangement that provides credit to customers for the retail value of the electricity their system generates Pacific Gas & Electric Photovoltaic Zero Net Energy A building with zero net energy consumption from the grid annually Domestic Hot Water Energy Use Intensity Outdoor Air Temperature Heat Pump Water Heater Institute for Transportation Studies Western Cooling Efficiency Center ii

5 FIGURES Figure 1. Operations Excellence... 4 Figure 2. Typical Apartment Installation In Progress Figure 3. Typical Apartment Installation Completed Figure 4. HVAC Trends 2013 and 2014 Monitoring Period Figure 5. Average HVAC Consumption 2013 and 2014 Monitoring Period Figure 6. HVAC Consumption with the Fan On Figure 7. Appliances Trends Figure 8. Total Appliance Consumption Figure 9. Plugs & Lights Trends Figure 10. Total Plugs and Lights Consumption Figure /7 Plug Load Figure 12. Improper DHW Operation Figure 13. Average Monthly DHW Electricity and Water Usage Figure 14. DHW Trends Figure 15.Total Consumption Trends Figure 16.Total Consumption Figure 17. PV Production Trends Figure 18. SunPower PV Dashboard Figure 19. Net Consumption Trends Figure 20. End Use Comparison Figure 21. End Use Comparison TABLES Table Modeled Energy Consumption vs Monitored Consumption April Through December... 2 Table Modeled Energy Consumption vs Monitored Consumption January through August... 2 Table 3. Typical Apartment Electrical Panel... 8 Table 4. Typical Expansion Apartment Logger Plan... 9 Table 5. Monitoring Plan Summary iii

6 EQUATIONS Equation 1. Instantaneous Current Equation 2. Instantaneous Power Equation 3. Consumption Equation 4. Power Factor Correction iv

7 CONTENTS EXECUTIVE SUMMARY 1 INTRODUCTION 5 BACKGROUND 6 Building System Description - Residential 6 Apartment Electrical Consumption and Production 6 Apartment Domestic Hot Water Consumption 7 Building System Description - Commercial 7 Lease and Recreation Center 7 The Hub 7 Western Cooling Efficiency Center 7 Institute of Transportation Studies 7 TECHNICAL APPROACH/TEST METHODOLOGY 8 RESULTS 15 Monitoring Plan 8 Residential Apartment Monitoring - Electrical 8 Residential Apartment Monitoring - Domestic Hot Water 10 Lease and Recreation Center Monitoring 11 Commercial Tenant Space Monitoring 11 Monitoring Plan Summary 11 Measurement & Verification Boundary 12 Data Collection and Processing 14 Confidence, Uncertainty, & Quality Control 14 Residential Apartments 15 Comparison to Model 15 HVAC 16 DHW 16 General 16 HVAC 16 Appliances 20 Plugs and Lights 21 Domestic Hot Water 24 Total Consumption 27 PV 29 Net Consumption 30 End Use Breakdown 31 Conclusions 33 RECOMMENDATIONS 34 APPENDIX A 36 i

8 APPENDIX B 1 APPENDIX C 4 ii

9 EXECUTIVE SUMMARY PROJECT GOAL This project examines the site net energy consumption of a sample of units in the UC Davis West Village community. The three primary goals are as follows: 1. Study energy consumption and production in order to determine if this West Village sample is achieving its zero net energy goals. 2. Determine the impact of technical interventions and resident behavior engagement activities. Technical interventions are actions that address equipment performance and resident behavior engagements are tools to educate and encourage residents to reduce their energy consumption. Residential behavior engagements were coordinated by Sustainable Design + Behavior. 3. Use the project findings to introduce additional behavioral and technical interventions to move the West Village site closer to ZNE consumption. PROJECT DESCRIPTION West Village is a new student housing development on the UC Davis campus. The development consists of a combination of residential multi-family and mixed-use construction. The development provides housing and amenities for approximately 2,000 students, faculty, and staff. This study examines the energy consumption and photovoltaic production for 126 out of 663 apartments to determine how closely the end-use averages (kwh/sqft/month) compare to the model predicted values for the site. The end-use categories are: heating ventilation and air conditioning (HVAC), plugs and lights, appliances, domestic hot water (DHW) generation, and photovoltaic production. Throughout the course of the 2013 (April through December) and the 2014 (January through August) monitoring periods the results for the monitored units were reported monthly and totaled for annual reporting. This was used to determine the net energy consumption of the monitored units and compare the actual energy performance to the predicted model performance. The actual energy performance of each end use is compared to the model to assist in identifying where equipment may not be performing as scheduled and where there are opportunities for resident behavior engagements. End-use energy data is used to frame resident engagement strategies. Control groups and test groups for various interventions are monitored and reported on to show the impact of the interventions. The energy consumption and production of the lease/rec center and three of the commercial suites are also being monitored to determine their performance and impact on the communities overall site zero net energy goals. This assessment and verification project was undertaken in an attempt to move the new construction market towards the state of California s energy policy goals of ZNE by 2020 for new residential construction and 2030 for new commercial construction. This program is the expansion of a pilot study that was conducted during the fall of 2012 with 12 test apartments and 12 controls apartments. 1

10 PROJECT FINDINGS/RESULTS This interim report contains results and analysis for the monitored residential units from the start of monitoring in April 2013 through August The results and analysis for the monitored commercial units will be included in the final report, to be issued in early An analysis of the impact of the residential engagement interventions will be included in a separate report, and referenced in the final report on the West Village performance monitoring, to be issued in early Based on the monitored residences, including the average domestic hot water energy consumption, this sample of West Village apartments did not achieve site ZNE. For the 2014 monitoring period from January through August the average consumption of all monitored units was 4.7 kwh/sqft/yr and the average PV generation was 3.8 kwh/sqft/yr, the monitored residential units are 80% ZNE. We are not reporting site ZNE for the 2013 monitoring period because DHW energy consumption was not monitored until December of Two drivers are causing the monitored apartments not to achieve site ZNE: (1) the equipment is not operating as scheduled, and (2) the residents are consuming more energy than was predicted in the energy model. HVAC is the largest end use consumption followed by DHW. HVAC is the largest end-use (kwh/sqft/yr) in terms of overall consumption, followed by DHW, plugs & lights, then appliances. DHW is the highest end-use in terms of percent deviation above model prediction, followed by HVAC, and plugs & lights. Appliances are the smallest end-use in terms of overall consumption and are the only end-use that was overpredicted by the model, all other end-uses were under-predicted. Table 1 shows the modeled energy consumption versus the monitored energy consumption for each of the logged end uses. The 2013 monitoring period spanned from April through December. Table 2 shows the modeled energy consumption versus the monitored energy consumption for each of the logged end uses and the total consumption for the monitored units. The 2014 monitoring period spanned from January through August data through December will be reported in the final report (in early 2015). TABLE MODELED ENERGY CONSUMPTION VS MONITORED CONSUMPTION APRIL THROUGH DECEMBER END USE MODELED CONSUMPTION MONITORED CONSUMPTION MONITORED COMPARED TO KWH/SQFT/YR KWH/SQFT/YR MODELED CONSUMPTION HVAC % More Plugs and Lights % More Appliances % Less TABLE MODELED ENERGY CONSUMPTION VS MONITORED CONSUMPTION JANUARY THROUGH AUGUST END USE MODELED CONSUMPTION MONITORED CONSUMPTION MONITORED COMPARED TO KWH/SQFT/YR KWH/SQFT/YR MODELED CONSUMPTION HVAC % More DHW % More Plugs and Lights % More Appliances % Less Total % More 2

11 There are several reasons why the sample of DHW consumption exceeded the model prediction: Occupants used more water than expected, Electric resistance backup was used more often than anticipated, and DHW heat pumps occasionally ran continuously to meet demand. By monitoring both electricity and water usage, we were able to distinguish higher water usage from equipment malfunctions. This monitoring has been useful because it shows when the DHW heat pumps were running continuously and when the electric resistance backup heat was used, allowing Carmel to improve their operation. Plugs and Lights are the next highest end use category and exceed the model prediction. Continuous plug loads such as mini refrigerators, entertainment centers, and space heaters significantly contributing to the end use consumption. Sustainable Design + Behavior developed and implemented residential behavior engagement programs with the goal of educating and encouraging residents to reduce their energy consumption. The residential engagements were developed to target specific end uses and residents based on our analysis. The impacts of the engagements will be analyzed and discussed in a separate report that will be release in early In addition to and in coordination with the resident behavior engagements, technical interventions to address equipment performance were identified and implemented. A select number of monitored units were placed in either a test group or the control group. The test group received the residential engagement or technical interventions and the control group did not receive the residential engagement or technical interventions. To evaluate the impact of the engagements and interventions, the energy consumption of the control and test groups were compared to one another and the whole monitored population. Initial review of the results to date of the resident behavior engagements and technical interventions indicates that they have both had an impact on reducing the energy consumption of the monitored units. The continued implementation of the residential behavior engagements and technical interventions will help the West Village community realize the ZNE goals. The residential engagements included a plug load pledge, door to door engagements, and competitions, among other communication activities. The plug load pledge provided residents information on how to reduce plug loads and had them sign a pledge to reduce their plug load energy consumption. The door to door engagement targeted units that were high energy consumers. The occupants were informed that they were consuming more energy than the average residents were. The occupants were provided with information on how to reduce their energy consumption. There were two distinct HVAC technical interventions. The first involved reprograming thermostats in the units to prevent residents from adjusting the set point outside a prescribed temperature range; for example, setting a cooling limit on the thermostat so residents could not cool below 68 F. The second involved rewiring the thermostats to prevent the fan on the indoor fan coil unit from operating when there was not call for heating and cooling. PROJECT RECOMMENDATIONS The following figure shows the monitored energy consumption for 2014 vs. the model predicted energy consumption and a conservative estimate of future energy consumption 3

12 and production if the existing residential behavior engagements and technical interventions are implemented throughout the community. Though the residential behavior engagements and technical interventions have shown savings, in order to reach ZNE more aggressive technical interventions and residential behavior engagements will have to be taken or more renewables will have to be installed Average Monthly Monitored vs Modeled kwh/sqft/month Model Predicted HVAC 0.12 DHW 0.05 PV Production 0.46 Plugs & Lights 0.12 Appliances 0.18 Monitored Population Average HVAC 0.21 PV Production 0.47 DHW 0.12 Plugs & Lights 0.15 Appliances 0.10 Potential Operations Excellence HVAC 0.19 PV Production 0.49 DHW 0.10 Plugs & Lights 0.14 Appliances kwh/sqft/month FIGURE 1. OPERATIONS EXCELLENCE The following recommendations are technical interventions and residential engagements to improve maintenance and operations to support achieving zero net energy performance. HVAC Temperature Settings Since HVAC is the largest end-use, for units consuming high amounts of energy, the next step is to get control of setpoints, occupancy scheduling, and set-backs, for example by limiting the frequency and duration of compressor cycling by using thermostat settings that include high and low limit lock-outs of 70F cooling and 75F heating. Carmel could encourage students to program the schedule of the thermostats and could even offer a service to program the thermostats for the students. HVAC Passive Strategies Once occupants have good control over the thermostat for both temperature and fan operation, the next opportunity for outreach and education is the use of natural ventilation, internal/eternal shading, and utilizing the ceiling fans in the bedrooms and living areas. It is likely that units that used extremely little HVAC during the baseline period are employing some of these strategies. A passive cooling residential engagement was conducted in August of The results of this engagement will be available in early 2015 and will be referred to in the final report on West Village 4

13 Improving Heat Pump Water Heater Performance Domestic hot water is the second largest end use. We have observed two scenarios where the equipment did not operate as intended. The first case occurs when the heat pump water heaters cause the units to trip out on high head pressure requiring a manual reset. This condition can sometimes last for weeks. A system of visual indicators (run lights) has been installed so staff can verify proper operation through visual inspection during normal facility maintenance rounds. When the heat pump shuts down, the system will rely solely on the electric resistance heaters, which can easily consume 50% more energy than the heat pumps. Though there are visual inspection have been installed they can be difficult for staff to see and this issue persists. Installing an alarm system to send notifications of when the DHW heaters have tripped could prevent the extended periods where the DHW systems are running in electric resistance backup mode. Maintain PV Inverter Uptimes During the monitoring period it was noted that inverters may go offline periodically for unknown reasons. Some inverters were noted to go down for extended periods, indicating that SunPower was originally only coming out for scheduled maintenance, and not responding directly to individual unit issues. Now, Carmel partners and SunPower have developed a dashboard reporting system to more closely monitor PV performance and to respond to correcting down inverters more quickly, thus maximizing the production side of the zero net energy equation. Carmel should continue to closely monitor the PV production of the arrays to ensure inverters do not go offline for extended periods. Cleaning of Photovoltaic Cells Due to farming in the area and relatively low rainfall during the summertime dust accumulates on the PV systems and remains all summer during what should be the peak PV performance months. Based on the monitored data a 10% production increase could be expected if the arrays were cleaned after local farms had completed planting and before the peak PV production months. Therefore we recommend cleaning the PV arrays once a year after the planting season to maintain optimal PV performance. Reducing Plug Loads A number of residents participated in the plug load pledge engagement by signing an agreement to reduce their plug load energy consumption by using power save features for PC s, and turning off and/or unplugging devices when not in use. Initial analysis indicates that plug load pledge participants have shown a reduction in their plug and lights energy consumption. These results will be discussed in a separate report in early We recommend that the plug load pledge effort be continued. INTRODUCTION West Village is a multi-family student housing development on the UC Davis campus that was completed in The development consists of a combination of residential multifamily and mixed-use construction. The development provides housing and amenities for approximately 2,000 students, and 500 faculty and staff families, built in three (3) phases: Ramble, Viridian, and Solstice. In an agreement between Carmel Partners and UC Davis a plan was developed for Carmel to design, build, own, and operate a portion of new student housing development on the UC Davis campus with a long term land lease. The result of these efforts has created the largest multi-family ZNE development in the country, and is meant as a test bed and a guide post for future developments as the state moves towards its 2020 ZNE goals. 5

14 In order to achieve ZNE performance high performance design and construction must be implemented to decrease the amount of energy consumed and the remaining energy needs are offset completely by renewables, in this case PV. It is increasingly apparent that strong maintenance & operations as well as residential engagement and behavior modification are required post occupancy to maintain or improve the project s chances of ZNE performance over time. These residential engagements must continually be implemented due to the high resident turnover rate in a student residential community. This assessment and verification program was undertaken in an attempt to move the new construction market towards the state of California s energy policy goals of ZNE (zero net energy) by 2020 for residential construction and 2030 for commercial construction. PG&E has funded this verification and assessment study through its Emerging Technologies Program. A 2012 pilot study preceded this verification and assessment study. This verification and assessment study has two main components. One is a measurement and verification component meant to provide data and analysis of energy performance, led by Noresco. The other is a residential engagement component which designs and tests intervention strategies to evaluate the impact of behavior modification efforts on energy performance. Sustainable Design + Behavior coordinated the residential engagements and Noresco provided analysis to measure the impact of these engagements. This study has the objectives of measuring and verifying actual energy performance of a sample of units post occupancy. The results are meant to inform multiple parties including: The design and construction team for this and future similar projects as to what aspects of the design and construction were effective and what design assumptions were valid and which may be improved. The owner/operator for this and similar future projects as to what maintenance and operations are required for upkeep and persistence of ZNE performance. PG&E as to encourage the industry and developers to move towards the ZNE goals by providing incentives, encouragement, and guidance as to what works. The UC Davis campus and the UC system more generally as to how to develop and operate their real-estate assets to further the state and the university systems sustainability goals. Other teams using occupant engagement and behavior modification as a standalone or integrated energy conservation measure. BACKGROUND BUILDING SYSTEM DESCRIPTION - RESIDENTIAL APARTMENT ELECTRICAL CONSUMPTION AND PRODUCTION Typical apartment configurations include 2, 3, & 4 bedroom apartments. Each bedroom includes a private bathroom, and all bedrooms in an apartment share common living space including: living room, dining room, kitchen, and laundry. Appliances are Energy Star rated and permanent lighting is compact fluorescent. Heating, ventilation, and air-conditioning are by a central heat pump per apartment. Ceiling fans are provided in the bedrooms and 6

15 common spaces. Each apartment has an electrical panel for distribution to end devices, input of photovoltaic energy from the PV inverter, and a net meter for connection to the utility grid. Each apartment has its own PG&E meter. APARTMENT DOMESTIC HOT WATER CONSUMPTION Apartments are grouped into buildings, typically having twelve (12) apartments per building. A common all-electric domestic hot water (DHW) plant serves each building. A house panel and meter are provided for each building and serve the domestic hot water plant, exterior lighting, and phone and data equipment. Each domestic hot water plant consists of a primary heat pump water heater and back-up electric resistance within storage tanks. BUILDING SYSTEM DESCRIPTION - COMMERCIAL LEASE AND RECREATION CENTER The lease and recreation center is a 17,000 sqft, two-story facility that houses the leasing offices, and the recreation center. The recreation center consists of a large fitness center, game room, and heated outdoor pool. Heating and cooling are provided by three (3) variable frequency heat pumps that serve thirteen (13) fan coil units throughout the facility. Seven exhaust fans provide building ventilation. Domestic hot water is provided by a 60 gallon electric storage water heater. The lease and recreation center has a switchboard that serves seven panels and the elevator. There is a PV system and a PG&E meter. THE HUB The Hub is a combination of a coffee shop, a restaurant, and a convenience store totaling 6,000 square feet. Other than the swimming pool, this facility has the only natural service at the West Village site, which is used for the restaurant cook line, kitchen domestic hot water, and make-up air. HVAC is provided by split system heat pumps. There is a PV system and a PG&E meter. WESTERN COOLING EFFICIENCY CENTER The Western Cooling Efficiency Center (WCEC) is a research office space combined with an environmental test chamber laboratory totaling 9,500 square feet. HVAC is provided by split system heat pumps. Auxiliary HVAC systems maintain space conditions within the test chamber, which is required for the testing of other subject HVAC systems. There is a PV system and a PG&E meter. INSTITUTE OF TRANSPORTATION STUDIES The Institute of Transportation Studies is a research office space totaling 6,200 square feet. The space is a typical office with some individual office on the perimeter and cubicles in open office areas. HVAC is provided by split system heat pumps. There is a PV system and a PG&E meter. 7

16 TECHNICAL APPROACH/TEST METHODOLOGY MONITORING PLAN RESIDENTIAL APARTMENT MONITORING - ELECTRICAL Apartments come in 2, 3, & 4 bedrooms configurations. There is one (1) dedicated arc fault circuit interrupter AFCI breaker and circuit per bedroom, based on the number of bedrooms. The size of the PV system associated with each apartment varies by the number of bedrooms, and roof space and orientation. Aside from the bedroom circuits and PV array size, all apartments have a similar electrical panel configuration and typical end-use categories for consumption and production: HVAC, Appliances, Plugs & Lights, and Photovoltaic. TABLE 3. TYPICAL APARTMENT ELECTRICAL PANEL CIRCUIT # DESCRIPTION BREAKER BREAKER DESCRIPTION CIRCUIT # 1 AFIC Bedroom #1 3 AFCI Bedroom #2 20 A 20 A Kitchen GFCI 2 20 A 20 A Microwave 4 5 On Q Rec 15 A 20 A Kitchen GFCI 6 7 Kitchen Lights 15 A 20 A Dishwasher/Dis posal 9 Smoke Detector 15 A 20 A Bath GFCI Bath Lights 15 A 20 A Hall Plugs Heat Pump 15 A 20 A Living Room Plugs 15 Heat Pump 2 Pole 20 A Washing Machine 17 Space - 20 A A/C Space - 2 Pole A/C Space - 30 A Dryer Wattnode 15 A 2 Pole Dryer Wattnode 2 Pole 50 A Range Photovoltaic 20 A 2 Pole Range Photovoltaic 2 Pole - Space 30 Each apartment logger installation consists of one (1) Onset Hobo U30 logger. This logger has battery and wall power, and can store data locally for wired or wireless data retrieval. Each expansion scope apartment logger installation includes: five (5) 2-channel amperage modules, and ten (10) current transformers (CT s). Of the ten (10) CT s, six (6) are 20 amp, and four (4) are 50 amp. The 50 amp CT s are used for measuring current on one leg of each 240V/2 pole load including; Heat Pump, A/C, Range, Dryer, and Photovoltaic, and all 120 V small appliances including; microwave, washing machine, dishwasher, garbage

17 disposal. 20 amp CT s are used for measuring amperage of the various 120V/1 phase loads including: receptacles, GFCI s, and lights. The CT s are installed in such a way as to capture amperage for each end-use, by phase, to the greatest extent possible. Since the end-uses of Plugs and Lights will always be combined in the bedrooms because of the AFCI circuit breaker, the end-uses of Plugs and Lights are grouped into the category of Plugs & Lights. HVAC and Appliances are similarly grouped together as categories. The one exception is that the refrigerator, which is an appliance, is always plugged into a kitchen GFCI receptacle. By capturing the kitchen GFCI circuit separately, the refrigerator load can be backed out of the Plugs & Lights category and added to the Appliances category. The data loggers record the instantaneous current for each CT at five (5) minute intervals. The loggers connect to the facilities wireless internet and upload the logged data to a server. The logged data can then be accessed and downloaded from this server. TABLE 4. TYPICAL EXPANSION APARTMENT LOGGER PLAN MODULE NUMBER CHANNEL CT SIZE PHASE CIRCUIT # S CIRCUIT NAME S1 50 A A 13, 18 Heat Pump, A/C S2 50 A A 26 Range S1 50 A A 29 Photovoltaic S2 50 A B 4, 8, 16 Dishwasher, Garbage Disposer, Microwave, Washing Machine S1 20 A A 1, 5 Plugs S2 20 A A 10, 14 Plugs S1 20 A B 7, 11 Lights S2 20 A B 3, 12 Plugs S1 20 A A 22 Dryer S2 20 A A 2, 6 Kitchen GFCI Loggers are installed on 126 apartments in the community of 663 units. This is a sample rate of 17.5%, which will provide a high level of confidence and a low level of uncertainty in the results. Units were selected to represent the various possible configurations: 2, 3, or 4 bedroom; 1st, 2nd, or 3rd floor; primary orientation N, S, E, W. Monitored units cover the Ramble, and Viridian, but not Solstice due to the timing of construction and this monitoring project. Each apartment building consists of three floors that have the same apartment layouts on each floor. In order to facilitate the installation schedule, apartments chosen for study were all chosen as stacks of three (3) apartments. The three (3) stacked apartments were installed simultaneously and the team moved to the next stack. 9

18 FIGURE 2. TYPICAL APARTMENT INSTALLATION IN PROGRESS FIGURE 3. TYPICAL APARTMENT INSTALLATION COMPLETED RESIDENTIAL APARTMENT MONITORING - DOMESTIC HOT WATER Each apartment building contains twelve (12) apartments. A central DHW system provides hot water for all the apartments within the building without electrical or water sub-metering. Unlike the other end uses that are centralized in each of the monitored units DHW is produced by a centralized DHW plant for each building. The Ramble DHW plants consist of a heat pump water heater (HPWH) with 113 kbtu/h heating capacity and two (2)120 gallon storage tanks with 54 kw of electric resistance heat capacity each. In December 2013 the following logging equipment was installed on fourteen (14) DHW systems. The fourteen systems were chosen as a representative sample of the DHW plants for the thirty four (34) apartment buildings where units were monitored. One (1) logger is installed for each 10

19 domestic hot water plant. Amperage is read for both electric resistance back-up water heaters and the primary heat pump water heater. Five (5) temperature sensors record: cold water in, hot water out, supply/return temperature to/from heat pump water heater, as well as an intermediate temperature between the electric resistance and heat pump water heater. A flow meter is also provided to measure consumption of domestic hot water from each plant. The data loggers record the data at five (5) minute intervals. The loggers connect to the facilities wireless internet and upload the logged data to a server. The logged data can then be accessed and downloaded from this server. LEASE AND RECREATION CENTER MONITORING The logger installation at the lease and recreation center consists of three (3) Hobo U30 loggers. Each logger has a battery and wall power, and can store data locally for wired or wireless data retrieval. Each logger includes seven (7) two-channel amperage modules, and fourteen (14) CTs. The equipment that was logged includes: heat pumps, packaged ac units, fan coils, plug receptacles, exhaust fans, and the elevator. In total four (4) panels, the elevator, and sixty-five (65) individual circuits were logged at five (5) minute intervals. Appendix C shows the logged circuits at the lease and recreation center. COMMERCIAL TENANT SPACE MONITORING The logger installation at the Hub consists of three (3) Hobo U30 loggers. Each logger includes seven (7) two-channel amperage modules, and fourteen (14) CTs. The equipment that was logged includes: lighting, heat pumps, exhaust fans, and restaurant equipment. In total three (3) panels and thirty-nine (39) individual circuits were logged. Appendix C shows the logged circuits at the Hub. The logger installation at the Western Cooling Efficiency Center (WCEC) consists of three (3) Hobo U30 loggers. Each logger includes four (4) to seven (7) two-channel amperage modules, and eight (8) to fourteen (14) CTs. The equipment that was logged includes: The T3 A, B, and C panels, lighting, and plug receptacles. In total three (3) panels and seventy (70) individual circuits were logged. Appendix C shows the logged circuits at the WCEC. MONITORING PLAN SUMMARY The following summary provides an overview of the entire project s measurement and verification plan including the timeline of installation and reporting. The Pilot Study installed equipment for and monitored twenty-four (24) pilot apartments and the lease/rec center. The logging period for the Pilot Study was from October 17, 2012 through November 26, This equipment will continue logging energy consumption through the end of The Expansion Phase installed equipment for and monitored an additional one-hundred and twenty (120) expansion apartments, as well as providing for three (3) commercial installations, and one (1) weather station. The monitored period includes Q thru the end of 2014 for the apartments and the weather station. The monitored period for 11

20 commercial spaces will be The expansion scope apartment installation differs from the pilot apartment installation in that two (2) additional channels are provided per apartment, eliminating the need to synthesize SunPower PV data back into monitored end-use consumption data. The Pilot Upgrade upgrades the original pilot apartment installations to match the expansion apartment installations. This change eliminated two (2) of the fifteen (15) proposed commercial loggers in lieu of upgrading twenty-four (24) pilots. Upgraded pilot apartment monitoring period is The Water Heater Monitoring adds monitoring for fourteen (14) domestic hot water plants by repurposing twelve (12) of the upgraded pilot apartment loggers and two (2) of the commercial loggers. TABLE 5. MONITORING PLAN SUMMARY PROJECT PHASE Pilot Study Expansion Scope (CO#1) Water Heater Monitoring (CO#3) MONITORED UNITS 24 Pilot Apartments 1 Lease/Rec Center 24 Pilot Apartments 1 Lease/Rec Center 120 Expansion Apartments 1 Weather Station 3 Commercial Tenant Spaces 14 Domestic Hot Water Plants NUMBER OF LOGGERS NUMBER OF CHANNELS (UNITS) MONITORING INTERVAL MONITORING PERIOD START MONITORING PERIOD END (Amperage) 5 minutes 10/17/12 11/26/ (Amperage) 5 minutes 10/17/12 11/26/ (Amperage) 5 minutes 11/26/ /31/ (Amperage) 5 minutes 11/26/ /31/ ,200 (Amperage) 5 minutes 4/1/ /31/ (Temp/RH/Wind Speed/Wind Direction/Irradiance) 5 minutes 4/1/ /31/ (Amperage) 5 minutes 1/1/ /31/ (3 Amperage/5 Temperature/Flow) 5 minutes 1/1/ /31/2014 MEASUREMENT & VERIFICATION BOUNDARY PG&E Energy Meter Measures power consumption/production from/to the grid continuously and reports demand, consumption, and cost on a monthly basis. Typical account services: Individual apartment meters at Ramble House panel meters for buildings at Ramble Individual apartment meters at Viridian Individual commercial/retail tenant meters at Viridian House panel meters for buildings at Viridian 12

21 Individual apartment meters at Solstice 1 House panel meters for buildings at Solstice 1 Facility meters for: main lease/recreation center, satellite recreation center (under construction), maintenance facility Covered parking PV system meters Site meters for: site and street lighting SunPower Energy Meters Measures power consumption/production continuously at two locations. One meter is in the same location as the PG&E meter (between the panel and the grid), and a second meter is between the PV system and the panel. Reporting of demand and consumption/production is available down to 5 minute intervals. End-use consumption is calculated as the sum of the two (2) meters, consumption from the grid and production from the PV system. NORESCO/U-30 Loggers Ramble Apartments - Measures current from ten (10) separate channels instantaneously, once every 5 minutes. Logger channels and electrical panel circuits are arranged to report by end-use (plugs & lights, appliances, HVAC, PV) down to 5 minute intervals. Ramble House Panels Measures current from three (3) separate channels and temperature from five (5) separate channels. Logger channels and electrical panel circuits are arranged to report by end-use (heat pump water heating, electric resistance water heating) down to 5 minute intervals. Viridian Apartments - Measures current from ten (10) separate channels instantaneously, once every 5 minutes. Logger channels and electrical panel circuits are arranged to report by end-use (plugs & lights, appliances, HVAC, PV) down to 5 minute intervals. Lease/Recreation Center Measures current from forty-two (42) separate channels instantaneously, once every 5 minutes. Logger channels and electrical panel circuits are arranged to report by end-use (plugs, lights, HVAC, and equipment) down to 5 minute intervals. Not all panels and circuits at the lease and recreation center were monitored. The circuits and panels that were metered were selected to calculate the aforementioned loads for the entire building using utility analysis methodologies with PG&E supplied meter data. Commercial/Retail Measures current from thirty (30) separate channels instantaneously, once every 5 minutes. Logger channels and electrical panel circuits are arranged to report by end-use (plugs, lights, HVAC, equipment) down to 5 minute intervals. Weather Station Measures temperature, humidity, light intensity, wind speed, wind direction instantaneously, once every 5 minutes. Reporting down to 5 minute intervals. 1 Not monitored as part of this project. 13

22 DATA COLLECTION AND PROCESSING Data logging equipment installed for this project measures instantaneous current as a proxy for instantaneous power. Raw data is measured and logged on 5 minute intervals in milli- Volts. This data is converted to instantaneous current (A) using Equation 1. EQUATION 1. INSTANTANEOUS CURRENT Instantaneous Current (A) = Logger Reading (mv) CT Scale Factor (50 A 333 mv) The instantaneous current is converted into instantaneous power using Equation 2. A voltage of 120 Volts and a power factor of 1.0 are assumed. For 240V/2 pole loads where one leg is being measured, the power is Equation 2, times 2. EQUATION 2. INSTANTANEOUS POWER Instantaneous Power (kw) = Intantaneous Current (A) Voltage (V) Power Factor (PF) (1 kw 1,000 W) The consumption of power over time is calculated using Equation 3. EQUATION 3. CONSUMPTION Consumption (kwh) = Intantaneous Power (kw) Interval (5 min) (1 Hour 60 min) Since the installed equipment is measuring current (A) as a proxy for power (kw) and consumption (kwh), with an assumed voltage and power factor, the accuracy of the result could be improved by using data from PG&E s utility meters, by apartment. To improve the accuracy of the current (A) readings, a power factor is calculated using Equation 4. EQUATION 4. POWER FACTOR CORRECTION Calculated Power Factor = PG&E (kwh) Hobo Consumption (kwh) The calculated power factor then is used to replace the assumed power factor in Equation 2. Results are carried forward into Equation 3. In this way the validity of results presented using current (A) as a proxy for power (kw) are improved. At the time of the writing of this report the PG&E utility data was not available for analysis. This method does introduce some error since the power factor would vary continuously (it is not constant over the period as assumed in the methodology above), and the power factor would be different for different end-uses even though the consumption occurs simultaneously (the methodology above assumes identical power factor regardless of enduse). The above methodology also does not consider variances in voltage (assumed constant as 120 V). Additionally, 5 minute interval data will not capture some short intermittent power uses (like operating the microwave for less than 5 minutes). However, the above method was deemed a reasonable balance of accuracy, cost, and M&V rigor. Future studies may contemplate more costly and/or more accurate methodologies on a benefit-cost basis. CONFIDENCE, UNCERTAINTY, & QUALITY CONTROL Two major sources of error exist in any study of this nature; they are measurement error and human error. Based on the published literature from the equipment manufacturer and an analysis of the calculation methodologies used, it will be possible to define the expected level of confidence and uncertainty in the results that are the result of the metering equipment. 14

23 A quality control process has been set up to limit sources of human error including: training for metering equipment installers, step-by-step installation check sheets, spot checking of logger readings against NIST traceable hand held meters, data filters to sort out values that are out of range or otherwise not as excepted, investigation and correction of suspect readings, CTs, or loggers. RESULTS RESIDENTIAL APARTMENTS This section discusses the monthly and total monitored results from the 2013 and 2014 monitoring periods as compared to the modeled consumption. The 2013 monitoring period was from April through November and the 2014 monitoring period was from January through August. The monthly data was evaluated, known data issues were resolved, and invalid data was removed. For this reason, the total consumption and PV production for the 2013 monitoring period include results for 69 of the units 140 units monitored in 2013 and 94 of the 126 units monitored in Appendix A shows a detailed table of all monitored units with the installation and removal dates and any data issues that resulted in removing data from analysis. Trends are presented in the following figures showing the average consumption/production by end-use over the course of the year. Trend lines are shown for the average consumption/production and the modeled consumption/production. Minimum and maximum outliers are shown for reference and are not necessarily the same apartment (trend) month to month. Additionally, histograms show the distribution of results and indicate how they compare to the model prediction (green bar). The trend figures for HVAC, plugs and lights, and appliances share the same axis scale to illustrate the difference in magnitude between the end-uses. The HVAC, plugs and lights, appliances, and total consumption histograms also have the same axis scale for comparison purposes. COMPARISON TO MODEL Davis Energy Group developed an energy model to predict the annual energy consumption of West Village based on the design drawings before the final built conditions were determined. The energy consumption predicted by this model was used by SunPower to design the PV array system to meet the West Village site ZNE goals. We are comparing to the model to identify areas where the actual energy consumption notably differed from the model predicted consumption. This information can be used to inform future model assumptions and the level of effort for energy models when they are used to for ZNE construction. Davis Energy Group developed the model with the best information available at the design stage. However, it is common for the built conditions to be slightly different. The following lists where as-designed model inputs differed from the final as-built conditions observed through this evaluation. 15

24 HVAC Model Assumed 0.18 W/CFM fan power. Assumed that the fan would be set to auto, however it was observed that numerous units had the fan set to on. A technical intervention during the project overrode this setting, only allowing the fans to be set to off or auto. Assumed no electric resistance heat. Assumed seasonal cooling lockout from 10/31-4/1, but actual operation did allow for cooling during this time. Auto sized heat pump units, which varied from the actual units installed. Assumed setbacks during school breaks, but there was higher occupancy than anticipated by the model. Assumed 70 F heating and 81 F cooling set points, but actual operation was not limited this way. A technical intervention during the project did eventually set heating and cooling limits on the thermostat. DHW Assumed electric resistance heat only to supplement heating during periods of high demand, but actual performance showed periods where the DHW heat pumps tripped, forcing electric resistance backup heat to be the sole source of DHW. During the project, monitoring both electricity and water usage allowed the team to identify this behavior. General Assumed 50% occupancy during school breaks, but actual summer occupancy was higher. Furthermore, a single student occupying a suite in the summer could use nearly as much energy as full occupancy. Assumed enhanced plug load controls. Assumed occupants acted as a family unit, but the behavior of students as a group is less predictable. HVAC The following figure shows the HVAC consumption per sqft for the 2013 and 2014 monitoring periods. The figure also shows the average outdoor air temperatures on the secondary axis. 16

25 kwh/sqft/month OAT F PG&E s Emerging Technologies Program HVAC Consumption kwh/sqft/month Average OAT F Average 2014 Average 2013 Max Unit 2014 Max Unit Model 2013 Average OAT 2014 Average OAT Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 FIGURE 4. HVAC TRENDS 2013 AND 2014 MONITORING PERIOD Over the entire monitoring period, the average monthly HVAC consumption is consistently running above the modeled predictions. However, the HVAC consumption decreased from 2013 to 2014 despite higher summer temperature in 2014 as compared to This is likely a result of the technical interventions and resident behavior engagement efforts that occurred over the course of the monitoring period. Over the 2013 monitoring period, the HVAC consumed 115% more energy than the model prediction, whereas during the 2014 monitoring period the HVAC consumed 82% more energy than the model prediction. For 2013 monitoring period, the HVAC average monthly consumption was 0.23 kwh/sqft/month, and the model had predicted consumption for that time period was 0.11 kwh/sqft/month. For the 2014 monitoring period, the average HVAC monthly consumption was 0.21 kwh/sqft/month, whereas for that same time period the model had predicted 0.12 kwh/sqft/month. The drop in consumption that occurs from March through May represents the shift between heating to cooling season. The 2013 summertime consumption in July and August were 226% and 219% greater than the model prediction compared to the 2014 summertime consumption in July and August were 199% and 151% greater than model prediction. 17

26 Percent of Monitored Population PG&E s Emerging Technologies Program Except for one (1) instance, the maximum consumers for the 2014 monitoring period consumed less HVAC energy than the maximum consumers during the 2013 monitoring period. In 2013 the highest HVAC energy consumer consumed 156% more HVAC energy than the average consumer and 425% more than the model. For the 2014 monitoring period the unit with the greatest HVAC consumption over the monitoring period consumed 127% more HVAC energy than the average consumer and 309% more than the model. The following figure shows the distribution of HVAC consumption for the 2013 and the 2014 monitoring periods. The green bar shows where the model predicted consumption for the metering period would fall. The full monitoring period sample for 2013 is 69 units and the full monitoring period sample for 2014 is 94 units. Appendix A shows a detailed table of all monitored units with the installation and removal dates and any data issues that resulted in removing data from the total annual analysis. Average HVAC Consumption kwh/sqft/month 50% 45% 43% 40% 35% 38% 36% 30% 25% 30% 2013, n= , n=94 20% 15% 10% 5% 10% 9% 7% 12% 6% 4% 4% 0% kwh/sqft/month FIGURE 5. AVERAGE HVAC CONSUMPTION 2013 AND 2014 MONITORING PERIOD For the 2013 monitoring period 14% of units consumed less than or equal to the model predicted consumption. This increased in 2014 to 16%. HVAC Fan Settings A common HVAC problem causing excessive HVAC consumption is the fan being on continuously instead of cycling on during compressor operation. The simplest fix is education and training on thermostat programming. The figure below shows a unit that has the thermostat initially set to the fan on setting, where the fan runs 18

27 kw PG&E s Emerging Technologies Program continuously, 24-hours a day. This significantly increases the HVAC energy consumption compared to a fan auto setting, which cycles the compressor and fan together only on a call for heating or cooling. A technical intervention that was tested was rewiring thermostats to prevent the fan on the indoor fan coil unit from running when there was not a call for heating or cooling. This intervention was implemented throughout the Ramble complex in July of At the same time, Carmel staff reprogrammed the thermostats to prevent the occupant from setting the cooling temperature below 68 F. 4 Test Unit HVAC Intervention /30/2013 9/25/2013 9/20/2013 9/15/2013 9/10/2013 9/5/2013 8/31/2013 8/26/2013 8/21/2013 8/16/2013 8/11/2013 8/6/2013 8/1/2013 FIGURE 6. HVAC CONSUMPTION WITH THE FAN ON The above figure shows a unit that received an initial HVAC intervention in 2013 during the summer student move out and the fall student move in. Before the summer move out this unit was consistently running the fan on the AHU even when there was no call for cooling. This unit then became unoccupied and there was not a call for cooling but the fan continued to run. Before the fall move in the thermostat was reprogrammed to not allow the fan to run when there was not a call for heating or cooling. After the technical intervention the figure shows that the fan is no longer running when there is not a call for heating or cooling. 19

28 kwh/sqft/month PG&E s Emerging Technologies Program APPLIANCES The following figure shows the energy consumption per square foot for appliances for the 2013 and 2014 monitoring periods. 1.2 Appliance Consumption kwh/sqft/month Average 2014 Average 2013 Max Unit 2014 Max Unit Model Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec FIGURE 7. APPLIANCES TRENDS During the 2013 and 2014 monitoring periods, the average appliance consumption was consistently below model prediction. For the 2013 monitoring period, Appliances consumed 47% less energy than the model prediction. During the 2014 monitoring period, Appliances consumed 43% less energy than the model prediction. For the 2013 monitoring period, the average monthly consumption for Appliances was 0.09 kwh/sqft/month, the model had predicted 0.18 kwh/sqft/month for that period. In 2014, the average monthly Appliance consumption was 0.10 kwh/sqft/month; the model had predicted 0.18 kwh/sqft/month. For the 2013 monitoring period, the maximum user consumed 83% more appliance energy than the average consumer but only 5% more than the model. The maximum appliance energy consumer in 2014 used 154% more appliance energy than the average consumer but only 45% more as compared to the model. The slight reduction during June is likely related to the school schedule (end of spring quarter) Appendix B contains the academic calendars for the 2013 and 2014 monitoring periods. 20

29 Percent of Monitored Population PG&E s Emerging Technologies Program The following figure shows the distribution of Appliance energy consumption for the 2013 and the 2014 monitoring periods. The green bars show where the model predicted consumption for the metering periods would fall. The full monitoring period sample for 2013 is 69 units and the full monitoring period sample for 2014 is 94 units. Appendix A shows a detailed table of all monitored units with the installation and removal dates and any data issues that resulted in removing data from the total annual analysis. 60% Average Appliance Consumption kwh/sqft/month 52% 52% 50% 48% 46% 40% 30% 2013, n= , n=94 20% 10% 2% 0% kwh/sqft/month FIGURE 8. TOTAL APPLIANCE CONSUMPTION For the 2013 monitoring period, 99% of users consumed less than or equal to what the model predicted in appliance consumption. This decreased to 98% for the 2014 monitoring period. PLUGS AND LIGHTS The following figure shows the energy consumption per square foot for Plugs and Lights for the 2013 and 2014 monitoring periods. 21

30 kwh/sqft/month PG&E s Emerging Technologies Program 1.2 Plugs and Lights Consumption kwh/sqft/month Average 2014 Average 2013 Max Unit 2014 Max Unit Model Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec FIGURE 9. PLUGS & LIGHTS TRENDS Over the 2013 and 2014 monitoring period, the average Plugs and Light energy consumption was consistently more than the model prediction. However, the Plugs and Light consumption decreased from the 2013 to 2014 monitoring period. This is likely a result of the residential engagement efforts that occurred over the course of the monitoring period. For the 2013 monitoring period, the Plugs and Lights consumption was 33% more than the model prediction. During the 2014 monitoring period the Plugs and Lights consumptions was 39% more than the model prediction. For the 2013 monitoring period, the average monthly consumption for plugs and lights was 0.16 kwh/sqft/month and the model consumption for that period was 0.12 kwh/sqft/month. For the 2014 monitoring period, the consumption was 0.15 kwh/sqft/month as compared to the model predicted of 0.11 for the kwh/sqft/month. The following figure shows the distribution of plugs and lights energy consumption for the 2013 and the 2014 monitoring periods. The green bars show where the model predicted consumption for the metering periods would fall. The full monitoring period sample for 2013 is 69 units and the full monitoring period sample for 2014 is 94 units. Appendix A shows a detailed table of all monitored units with the installation and removal dates and any data issues that resulted in removing data from the total annual analysis. 22

31 Percent of Monitored Population PG&E s Emerging Technologies Program Average Plugs and Lights Consumption kwh/sqft/month 90% 80% 78% 70% 60% 62% 50% 40% 2013, n= , n=94 30% 20% 19% 15% 10% 7% 10% 4% 4% 0% kwh/sqft/month FIGURE 10. TOTAL PLUGS AND LIGHTS CONSUMPTION For the 2013 metering period 30% of units consumed less than or equal to the model predicted consumption. This decreased to 24% for the 2014 monitoring period. The green bar represents where the model consumption would fall in this histogram. Not every consumer in this bin is less than or equal to the model. The plug load pledge was a residential engagement that provided residents information on how to reduce plug loads and had them sign a pledge to reduce their plug load energy consumption. The following figure shows the elimination of a continuous plug load after the residential engagement activity took place. 23

32 kw PG&E s Emerging Technologies Program Test Unit Plug Load Pledge Intervention /30/ /25/ /20/ /15/ /10/ /5/ /31/ /26/ /21/ /16/ /11/ /6/ /1/2013 FIGURE /7 PLUG LOAD The above figure shows a unit that participated in the plug load pledge. This figure show that the occupants in this space were able to eliminate a constant load on this circuit thereby reducing their overall energy consumption. DOMESTIC HOT WATER Unlike the other end uses that are centralized in each of the monitored units DHW is produced by a centralized DHW plant for each building. The Ramble DHW plants consist of a heat pump water heater (HPWH) with 113 kbtu/h heating capacity and two (2)120 gallon storage tanks with 54 kw of electric resistance heat capacity each. The electric resistance heaters are intended to provide backup to the HPWHs during times when the HPWHs cannot meet the DHW demand. We have observed several periods during the 2014 monitoring period when various DHW plants were relying solely on the electric resistance of the storage tanks to produce DHW. This occurs when the HPWH is not operating as scheduled and trips off due to high head pressure. When this happens these units will remain off, and the plant will rely solely on the electric resistance backup heat, until the HPWH is manually reset. In order to address this, a system of visual indicators (run lights) was installed so staff can verify proper operation through visual inspection during normal facility maintenance rounds. However, over the monitoring period there have been instances where several days will pass before the HPWH 24

33 kw PG&E s Emerging Technologies Program is manually reset. When a DHW plant is relying solely on electric resistant heat the system can consume 50% or more energy in one day than when only the HPWH provides the DWH. We have also observed instances where the HPWH will run continuously for extended periods. It is unclear from our study what causes this behavior. However, when this type of operation can consume approximately 175% more energy than when the heat pump is cycling and nearly 100% more energy than if the system was relying solely on electric resistance heat. The following figure shows a DHW system that is experiencing both of the above problems. These issues are a result of equipment issues as opposed to operator- or user-caused issues. 60 Improper DHW Operation Improper Operation Continuous heat pump operation. 10 Improper Operation Electric resistance heat meeting all DHW needs. Proper Operation heat pump cycling EWH 1 Heat Pump 0 4/1/2014 3/27/2014 3/22/2014 3/17/2014 3/12/2014 3/7/2014 3/2/2014 2/25/2014 FIGURE 12. IMPROPER DHW OPERATION Figure 13 shows the monthly average DHW electricity and water usage in kwh per square foot per day (kwh/sf/day) and gallons per person per day (gal/person/day), respectively for two of the fourteen monitored DHW plants. On the left is an example of the behavior that we expected to see, where electricity and water usage track together. On the right, in April we see an instance where electricity usage increases while water usage decreases. This is likely due to the system relying on electric resistance backup heat. However, by May this behavior is corrected, probably due to maintenance after visual inspection during rounds. 25

34 kwh/sqft/month PG&E s Emerging Technologies Program FIGURE 13. AVERAGE MONTHLY DHW ELECTRICITY AND WATER USAGE The following figure shows the DHW energy consumption per square foot for the monitored Ramble DHW plants over the 2014 monitoring period. DHW was not monitored during the 2013 monitoring period Ramble DHW kwh/sqft/month Average Maximum System Model Minimum System Jan Feb Mar Apr May Jun Jul Aug FIGURE 14. DHW TRENDS The average DHW consumption is consistently running above model prediction. During the months of January and March there were four (4) DHW plants that were operating solely on electric resistance backup heat for extended period of time. In 26

35 kwh/sqft/month PG&E s Emerging Technologies Program February there were five (5) units that were not properly operating. From April through August of the monitored units only one or two DHW plants were not operating a scheduled. For the 2014 monitoring period, DHW on average consumed 132% more energy than the model predicted. There is only one monitored DHW plant that has consumed less than or equal to the model predicted consumption over the 2014 monitoring period. This is shown on the above figure as the minimum system trend line. TOTAL CONSUMPTION The following figure shows the average total energy consumption of the monitored units compared to the model predicted consumption for the 2013 and 2014 monitoring periods. The 2014 monitored and model predicted energy consumption include DHW consumption whereas the 2013 monitored and model predicted energy consumption does not include DHW consumption. The units with the minimum and maximum total consumption are displayed. 1.6 Total Energy Consumption kwh/sqft/month HVAC 2013 Plugs and Lights Appliance 2014 DHW HVAC 2014 Plugs and Lights Appliances 2014 Min Unit Min Unit 2014 Max Unit Max Unit 2014 Model Model (excludes DHW) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 FIGURE 15.TOTAL CONSUMPTION TRENDS For both the 2013 and 2014 monitoring period, the total consumption is greater than the model predicted. 27

36 Percent of Monitored Population PG&E s Emerging Technologies Program Excluding DHW for the 2013 and 2014 monitoring periods, the average total consumption for 2013 was 19% more than modeled, this decreased to 16% over the model predicted for the 2014 monitoring period. Excluding DHW for the 2013 and 2014 monitoring periods, the average total consumption for the 2013 monitoring period was 0.48 kwh/sqft/month. The average total consumption for the 2014 monitoring period was 0.47 kwh/sqft/month. The model predicted average total consumption for the 2013 and 2014 monitoring periods is 0.40 kwh/sqft/month. The following figure shows the distribution of Total energy consumption for the 2013 and the 2014 monitoring periods excluding the DHW consumption. The green bars show where the model predicted consumption for the metering periods would fall. The full monitoring period sample for 2013 is 69 units and the full monitoring period sample for 2014 is 94 units. Appendix A shows a detailed table of all monitored units with the installation and removal dates and any data issues that resulted in removing data from the total annual analysis. 35% Average Total Consumption Excluding DHW kwh/sqft/month 30% 26% 29% 28% 25% 20% 22% 22% 21% 15% 13% 2013, n= , n=94 10% 7% 9% 7% 6% 5% 4% 3% 1% 1% 0% kwh/sqft/month FIGURE 16.TOTAL CONSUMPTION For the 2013 monitoring period 36% of monitored units consumed less than or equal to the model predicted when DHW consumption is excluded. For the 2014 monitoring period, excluding DHW consumption, 35% of monitored units consumed less than or equal to the model predicted. The green bar represents where the model 28

37 consumption would fall in this histogram. Not every consumer in this bin is less than or equal to the model. Of the 10 highest total users, of the 94 units with continuous data, seven (7) were among the top 10 HVAC users and five (5) were among the top 10 plug and light users. PV The following figure shows the equivalent full capacity production hours of energy production PV arrays over the monitoring period. The total solar irradiance over the metering period is also shown PV Production kwh produced /kw capacity Solar Irradiance kw/m PV Production kwh produced /kw capacity Solar Irradiance kw/m Average 2014 Average 2013 Min Unit 2014 Min Unit 2013 Max Unit 2014 Max Unit 2013 Total Solar 2014 Total Solar Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec 0 FIGURE 17. PV PRODUCTION TRENDS PV production appears normal; it is peaking in summer and reduces throughout the fall into winter following the general shape of the total solar irradiance. The lowest PV producer during the 2014 monitoring period is the same unit. This is also the same unit for the July through December 2013 monitoring period. The minimum PV producers in April through June were units where the inverters had gone offline for an extended period. 29

38 In early 2014 Carmel received a dashboard from SunPower to monitor PV production. This has enabled Carmel to respond quickly if a PV invertor goes offline and prompt SunPower to fix the problem resulting in more consistent PV product as compared to Figure 18 shows portion of this dashboard that reports overall performance. The dashboard also included detailed information by unit. The three arrays with the lowest average equivalent full capacity production hours had maximum outputs that were 49% to 61% of their rated capacity during the month of June, when there was the most solar irradiance. FIGURE 18. SUNPOWER PV DASHBOARD NET CONSUMPTION The following figure shows the projected total monthly net consumption for the Ramble complex. The projected net was determined by extrapolating the average monthly net consumption of all monitored units with valid PV data for each month to all units with the same square footage for the four square footages found in the Ramble. 30

39 kwh/month PG&E s Emerging Technologies Program Ramble Monitored Net kw/month Consumption Production Net Consumption Jan Feb Mar Apr May Jun Jul Aug FIGURE 19. NET CONSUMPTION TRENDS The total net consumption of the monitored units for the 2014 monitoring period is 111,707 kwh. The total projected net consumption for the entire Ramble complex for the 2014 monitoring period is 468,536 kwh Based on this calculation for the monitored Ramble units, the West Village apartments did not achieve site ZNE. For the 2014 monitoring period from January through August the average consumption of all monitored units was 4.7 kwh/sqft/yr, the average PV generation was 3.8 kwh/sqft/yr, and the monitored residential units are 80% ZNE. END USE BREAKDOWN Figure 20 shows the end use breakdown of the average monthly consumption for the 10 highest and lowest consumers as well as the population average. This DHW consumption is the same for each group because for our DHW analysis we applied the same monitored average consumption to all monitored units. For this reason, we excluded DHW consumption in the following end use consumption breakdown. 31

40 kwh/sqft/month PG&E s Emerging Technologies Program Monitoring Period Average Monthly Consumption kwh/sqft/month Appliances Plug and Lights HVAC Lowest 10 Consumers Population Average Highest 10 Consumers FIGURE 20. END USE COMPARISON Excluding DHW, for the lowest 10 consumers HVAC contributes to 36% of the total consumption. Excluding DHW, for the highest 10 consumers HVAC contributes to 46% of the total consumption. Each end use consumption, excluding DHW, increases from the 10 lowest consumers through the highest 10 consumers. 32

41 kwh/sqft/month PG&E s Emerging Technologies Program 0.9 May through August Average Monthly Consumption kwh/sqft/month Plugs & Lights 2014 Appliances 2014 HVAC 2013 Plugs & Lights 2013 Appliances HVAC Lowest 10 Consumers Population Average Highest 10 Consumers FIGURE 21. END USE COMPARISON For the lowest 10 consumers from 2013 to 2014, the HVAC consumption decreased 2%, appliance consumption decreased 35%, and plugs and light consumption decreased 41% For the population average from 2013 to 2014, HVAC consumption decreased 15%, appliance consumption decreased 9%, and plugs and light consumption decreased 7%. In contrast to the trend for the lowest 10 consumers and the population average the total consumption for the highest 10 users increased from 2013 to This is a result of these units using 45% more appliance energy and 16% more plugs and light energy. CONCLUSIONS The overall 2014 findings based on the results of the residential monitoring are as follows. Discussions of the possible causes of discrepancies between modeled predications are included in the subsequent sections. For the 126 units monitored: HVAC is the largest end-use and exceeds the model predicted consumption by 82%. 33

42 DHW is the second largest end-use and is the end use that most exceeds the model prediction. The monitored consumption exceeds the model by 132%, primarily due to DHW heat pumps operating incorrectly. Plugs & Lights are on average consuming 39% above the model prediction. Appliances are the smallest end-use and are on average consuming 43% less energy than the model prediction. The savings in this end-use is helping to offset overages in other end-uses. Total consumption is tracking above model prediction. Net consumption is currently 80% of the way to ZNE. There have been a number of residential engagement activities, focused on technical interventions and behavior interventions, led by Chris Hammer. Early analysis indicates energy savings; the full results will be released in early RECOMMENDATIONS The following recommendations are technical interventions and residential engagements to improve maintenance and operations to support achieving zero net energy performance. HVAC Temperature Settings Since HVAC is the largest end-use, for units consuming high amounts of energy, the next step is to get control of setpoints, occupancy scheduling, and set-backs, for example by limiting the frequency and duration of compressor cycling by using thermostat settings that include high and low limit lock-outs of 70F cooling and 75F heating. Carmel could encourage students to program the schedule of the thermostats and could even offer a service to program the thermostats for the students. HVAC Passive Strategies Once occupants have good control over the thermostat for both temperature and fan operation, the next opportunity for outreach and education is the use of natural ventilation, internal/eternal shading, and utilizing the ceiling fans in the bedrooms and living areas. It is likely that units that used extremely little HVAC during the baseline period are employing some of these strategies. A passive cooling residential engagement was conducted in August of The results of this engagement will be available in early Improving Heat Pump Water Heater Performance Domestic hot water is the second largest end use. We have observed two scenarios where the equipment did not operate as intended. The first case occurs when the heat pump water heaters cause the units to trip out on high head pressure requiring a manual reset. This condition can sometimes last for weeks. A system of visual indicators (run lights) has been installed so staff can verify proper operation through visual inspection during normal facility maintenance rounds. When the heat pump shuts down, the system will rely solely on the electric resistance heaters, which can easily consume 50% more energy than the heat pumps. Though there are visual inspection have been installed they can be difficult for staff to see and this issue persists. Installing an alarm system to send notifications of when the DHW heaters have tripped could prevent the extended periods where the DHW systems are running in electric resistance backup mode. 34

43 Maintain PV Inverter Uptimes During the monitoring period it was noted that inverters may go offline periodically for unknown reasons. Some inverters were noted to go down for extended periods, indicating that SunPower was originally only coming out for scheduled maintenance, and not responding directly to individual unit issues. Now, Carmel partners and SunPower have developed a dashboard reporting system to more closely monitor PV performance and to respond to correcting down inverters more quickly, thus maximizing the production side of the zero net energy equation. Carmel should continue to closely monitor the PV production of the arrays to ensure inverters do not go offline for extended periods. Cleaning of Photovoltaic Cells Due to farming in the area and relatively low rainfall during the summertime dust accumulates on the PV systems and remains all summer during what should be the peak PV performance months. Based on the monitored data a 10% production increase could be expected if the arrays were cleaned after local farms had completed planting and before the peak PV production months. Therefore we recommend cleaning the PV arrays once a year after the planting season to maintain optimal PV performance. Continue Reducing Plug Loads A number of residents participated in the plug load pledge engagement by signing an agreement to reduce their plug load energy consumption by using power save features for PC s, and turning off and/or unplugging devices when not in use. The plug load pledge participants have shown a reduction in their plugs and lights energy consumption. These results will be discussed in a separate report in early We recommend that the Plug Load Pledge effort be continued. 35

44 Unit ID Pilot? 1 Y Y 2 Y 3 Y Y APPENDIX A Moved to WH? Sep-13 Oct-13 Nov-13 Dec-13 Jan-14 Feb-14 Mar-14 Apr-14 May-14 Jun-14 Jul-14 Aug-14 Channels 9 actually the range and channel 10 actually HVAC were labeled in reverse. This has been corrected for this month but remains true for previous months. Incomplet e data set. We were only able No Data. No Data No Data No Data No Data 36

45 7 Y Y 8 Y 9 Y Y Y Y 14 Y 15 Y Y to retrieve four days of data for the month. This site is not included in the analysis. 21 No Data No Data No Data

46 24 Channel 5 Plugs and Lights experienci ng sensor error short/open. This data has been removed from the September analysis. a) Channel 9 actually the range and channel 10 actually HVAC were labeled in reverse. This has been corrected for this month but remains true for previous months b) Channel 5 Plugs and Lights experienci ng sensor error short/open. This data has been removed from the October analysis. Channel 5 Plugs and Lights experienci ng sensor error short/open. This data has been removed from the November analysis. December service call addressed: Channel 5 Plugs and Lights were experienci ng sensor error short/open. This data has been removed from the December analysis. Before May the Dryer and GFI data were in Current this has been fixed in the analysis moving forward. Before May the Dryer and GFI data were in Current this has been fixed in the analysis moving forward

47 29 No Data No Data Y Y Channel 2 has Small Appliance s and the Dryer. This channel is scaled as if all end uses were 120 V Channel 7 PV is corrupted; data never goes to zero and Dryer and Kitchen GFI were labeled and scaled in reverse. This has been addressed in this month s analysis and moving forward. Data for three sensors not exporting Data for three sensors not exporting Data for three sensors not exporting 39

48 doesn t follow the normal PV pattern. This data has been removed from the November analysis Channel 3 Dryer and Channel 9 HVAC are showing the exact same usage patterns. Likely two CTs installed on one circuit and one is mislabele d. This data has been removed from the September analysis. Channel 3 Dryer and Channel 9 HVAC are showing the exact same usage patterns. Likely two CTs installed on one circuit and one is mislabele d. This data has been removed from the October analysis. Channel 3 Dryer and Channel 9 HVAC are showing the exact same usage patterns. Likely two CTs installed on one circuit and one is mislabele d. This data has been removed from the November analysis. December service call addressed: Channel 3 Dryer and Channel 9 HVAC are showing the exact same usage patterns. Likely two CTs installed on one circuit and one is mislabele d. This data has been removed from the 40

49 December analysis Y Y No Data. Unknown issue. Channel 8 has Small Appliance s and the Dryer. In previous months this has been scaled as if all end uses were 240V. To be consistent this has been changed in the November analysis as if all end uses were 120V. 41

50 41 42 Channel 6 plugs and lights experienci ng sensor error short/open. This data has been removed from the September analysis. No Data. Unknown issue. No Data Small Appliance Channel is double metering the Kitchen GFI channel. This data remains in the December analysis. As the total appliance consumpti on for this site with this data is the average appliance consumpti on for this month. No Data No Data No Data 43 No Data No Data No Data 44 No Data No Data No Data Y Y Channel 3 has both Small Appliance s and the Dryer metered by this Channel 3 has both Small Appliance s and the Dryer metered by this Channel 4 has both Small Appliance s and the Dryer metered by this 45 sensor. In sensor. In sensor. 42

51 previous months this was scaled as if all of the end uses were 240 V. This channel was rescaled as if all end uses were 120 V. previous months this was scaled as if all of the end uses were 240 This channel was rescaled as if all end uses were 120 V in xlsx. This channel is scaled as if all end uses were 120 V. 49 No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data No Data 43

52 65 No Data. Unknown issue. No Data December service call addressed communic ation issue. Partial month data has been removed from the December analysis. 66 No Data No Data No Data Channels 9 actually the range and channel 10 actually HVAC were labeled in reverse. This has been corrected for this month but remains true for previous months. 44

53 70 71 Channel 1 Kitchen GFI, which has the refrigerato r, went to zero for approxima tely one week. This data was left in the September analysis because it is unknown why the data went to zero for one week. 72 Y No Data No Data 73 Channel 3 Dryer and Channel 7 HVAC are showing the exact same usage patterns. Likely two CTs installed on one circuit and one is December service call addressed: Channel 3 Dryer and Channel 7 HVAC were showing the exact same usage patterns. Likely 45

54 74 75 mislabele d. This data has been removed from the November analysis. Channel 1 Dryer and Channel 9 HVAC are showing the exact same usage patterns. Likely two CTs installed on one circuit and one is mislabele d. This data has been removed from the November analysis. two CTs installed on one circuit and one is mislabele d. This data has been removed from the December analysis. December service call addressed: Channel 1 Dryer and Channel 9 HVAC are showing the exact same usage patterns. Likely two CTs installed on one circuit and one is mislabele d. This data has been removed from the December 46

55 analysis Y 78 Channel 1 Dryer and Channel 9 HVAC are showing the exact same usage patterns. Likely two CTs installed on one circuit and one is mislabele d. This data has been removed from the November analysis. December service call addressed: Channel 1 Dryer and Channel 9 HVAC are showing the exact same usage patterns. Likely two CTs installed on one circuit and one is mislabele d. This data has been removed from the December analysis. 47

56 79 Hobolink is duplicatin g the data from the logger at 77 as the data for this site. We removed this site from analysis. Hobolink is duplicatin g the data from the logger at 77 as the data for this site. We removed this site from analysis. Hobolink is duplicatin g the data from the logger at 77 as the data for this site. We removed this site from analysis. No Data No Data No Data No Data No Data No Data No Data 80 No Data No Data No Data 81 Channels 5 and 6 both Plugs and Lights experienci ng sensor error short/open. This data has been removed from the September analysis. Channels 5 and 6 both Plugs and Lights experienci ng sensor error short/open. This data has been removed from the October analysis. Channels 5 and 6 both Plugs and Lights experienci ng sensor error short/open. This data has been removed from the November analysis. 82 Y 83 No Data No Data No Data No Data. Unknown issue. No Data December service call addressed communic ation issue. 48

57 86 87 Partial month data has been removed from the December analysis. 88 No Data No Data No Data This was a logger that was not connectin g before. It is communic ating now. Communi cation Y started 89 10/8 therefore the analysis for this month has one week of extrapolat ed data. No Data No Data No Data 90 No Data No Data No Data 91 No Data No Data No Data Y 95 49

58 No Data No Data No Data 99 Y No Data No Data No Data No Data No Data Y Y Y No Data No Data No Data No Data. Unknown issue. No Data. Unknown issue. No Data No Data December service call addressed communic ation issue. Partial month data has been removed from the December analysis. No Data No Data No Data December service call addressed communic ation issue. No Data No Data No Data 50

59 Partial month data has been removed from the December analysis No Data. Unknown issue. No Data Channel 6 Dryer does not appear to be labeled properly. The data showed that it cycles on for extended periods of time on a consistent schedule. This data has been removed from the September analysis. Channel 6 Dryer does not appear to be labeled properly. The data showed that it cycles on for extended periods of time on a consistent schedule. This data has been removed from the October analysis. a) Channel 5 kitchen GFI shows no usage. This likely contribute s to this site being one of the lowest appliance users. b) Channel 6 Dryer does not appear to be labeled properly. The data showed that it 51

60 119 Y Y 120 PG&E s Emerging Technologies Program cycles on for extended periods of time on a consistent schedule. This data has been removed from the November analysis. 121 No Data Y Y Many channels reading zero. Confirm whether apartment is currently occupied. Channel 5 states that the refrigerato r is on this channel. However, it appears that This apartment is unoccupie d. This apartment is unoccupie d. This apartment is unoccupie d. This apartment is unoccupie d. 52

61 Channel 10 Small Appliance s is metering the refrigerato r. This does not impact this analysis but should be addressed during service call. 127 No Data No Data No Data Y Y Channel 3 states that the refrigerato r is on this channel. However, it appears that Channel 10 Small Appliance s is metering the refrigerato r. This 53

62 does not impact this analysis but should be addressed during service call. 132 No Data No Data No Data No Data No Data 136 No Data No Data No Data No Data No Data No Data 139 In previous months Channel 6 was labeled Small App. However, this channel meters plugs and lights as well as the disposal. This channel was recategorize d as plugs In previous months Channel 6 was labeled Small App. However, this channel meters plugs and lights as well as the disposal. This channel was recategorize d as plugs Channel 6 channel meters plugs and lights as well as the disposal. This channel is categorize d as plugs and lights in the analysis because of the constant load that is on the circuit. 54

63 and lights because of the constant load that is on the circuit. and lights in the analysis because of the constant load that is on the circuit. 55

64 APPENDIX B Academic calendars pulled from 1

65 2

66 3