Getting Started with Your First Project: Considering Rules

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Amberlynn Ward
6 years ago
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1 Getting Started with Your First Project: Considering Rules to Deploy So you re ready to start a pilot project with a new customer. You have identified what data is available and where it is and how you will connect, now its time to scope the rules you will apply. If you have attended any of our training you know that we strongly recommend that you start with a clearly defined, fast- to- implement initial project scope that will drive quick results and savings. Create success for you and your customer and the resulting benefits will typically lead to ongoing expansion of your analytics project and a very happy customer. So where do you start? What analytic rules should you consider starting with? Available Data Influences Effective Analytic Rules The analytic rules you can deploy are always related to the available data available so that s the first question to answer what data is available? For example, if we have only energy consumption data available (KW and KWh), rules can be used to identify patterns representing issues like buildings running 24 hours a day, starting early or running too late. The Duke Realty case study provides an excellent example of the benefits that can be derived from this limited amount of data and some basic rules. You can find it here: Study- Duke- Realty Buildings- Magazine- Article- PDF.pdf This application note shows how to write a rule to identify the signature of buildings that are not exhibiting occupancy- based transitions in energy consumption which likely means that buildings are not operating on expected schedules. Note- Occupancy- vs- Kw- Function.pdf Into Equipment Data If we have access to data from HVAC equipment systems we can start with a range of common, but extremely useful, rules that are included in the SkySpark HVAC and Equip extension libraries. Some examples include: SkyFoundry Find What Matters 1

2 Detect improper operation of economizers with the ahucoolandecon and ahuheatandecon analytic functions. Identifying simultaneous Heating and Cooling, and Short Cycling of equipment: ahucoolandheat ahucoolheatcycling Consider applying the temp sensorfailure rule to identify temperature sensors that are not reading new values. It s easy to implement this rule function on a wide range of sensors and it typically produces surprising results. Here s a great case study showing the impact of non- functioning air pressure sensors on VAV fan systems: Study- Real- World- Example- - - Application- of- Analytics- in- a- Hospital.pdf You can find SkySpark s library of HVAC and Equip- oriented analytic functions here: hvac/index equip/index Industry Sources for Rule Descriptions A number of government labs and industry associations have weighed in on useful analytic rules for HVAC systems. And because SkySpark is fully programmable, you can implement virtually any rule you can define. As food for thought, here are some examples from a recent workshop by the US Dept of Energy, all of which can all be implemented as SkySpark rules. You can access the full report here: Workshop- 1- Summary pdf Air- Side Diagnostics for packaged rooftop units (RTUs) and built- up Air Handling Units (AHUs) Note: This example is very similar to SkySpark s built in HeatandEcon, CoolandEcon, and sensorfailure analytic functions, but is worth reviewing here SkyFoundry Find What Matters 2

3 Description: Large RTUs and AHUs (>7.5 ton in some case even >5 tons) are required to have air- side economizers, which may fail to perform as designed and lead to increased energy consumption. In many cases, the failure goes undetected because mechanical cooling compensates for the failure of the economizer and the failure does not direct impact the comfort of the occupants. Info Gained: There are number of air- side economizer faults that can be detected. These faults can be grouped into five categories: 1. Non- modulating damper 2. Temperature sensor problems (including missing and out of range sensor values) 3. Economizer operating when it should not (conditions not favorable for economizing) 4. Economizer not operating when it should (conditions favorable for economizing) 5. Ventilation greater than needed, and 6. Inadequate ventilation Required data: The measured data required to detect faulty economizer operations include: Mixed air, return air, and outdoor- air temperatures (and enthalpies for enthalpy- controlled economizers) Damper signal, Supply fan on/off status, and Heating and cooling on/off status or heating and cooling valve signal The measured data can be at any interval but preferably 1- minute (1- minute, 5- minute, half- hourly, or hourly, etc.). Equipment Long Cycling (Note: SkySpark provides an equipment short cycling and long cycling functions) Description: Detect long on- times, which may indicate that equipment is undersized or capacity has degraded. Info Gained: Whether system stays on too long and is not responsive to changes in load Required data: System status or a control signal, with recommended sampling rate of at least five minute frequency. Issues with Zone or Room Thermostat Deadbands Description: Small differences between the heating and cooling set points may lead to excessive cycling between heating and cooling modes increasing the overall energy consumption. SkyFoundry Find What Matters 3

4 Info Gained: By checking the heating and cooling set points and the zone/room temperatures the actual deadbands can be inferred and suggestions provide to make changes Required Data: Zone temperatures and zone heating and cooling set points Note: This case study shows an example of detecting cycling due to incorrect deadband settings: Study- Real- World- Example- of- the- Power- and- Benefits- of- Analytics.pdf Comfort Conditioning Performance Description: Compares indoor temperatures against defined ASHRAE comfort ranges and indicate when the comfort conditions are not being met Info Gained: Whether the building is too hot or too cold or outside the ASHRAE comfort range Required data: Time series history of indoor zone air temperature, and Hours of occupancy - Comfort ranges to be compared against Excessive Mode Transitions Description: Detects/indicates when the total number of mode transitions (i.e, mode changes per hour) exceeds a predetermined threshold Info Gained: Whether there are too many mode switches Required Data: Status of equipment from which SkySpark can detect the number of mode transmissions per hour (or other period of time). VAV Diagnostics: Airflow Rate Checks Description: Detects issues such as: Scheduling conflict with AHU, Tuning problem with airflow feedback control loop, Airflow sensor failure, Damper stuck or failed, Damper actuator stuck or failed, Supply air static pressure too low, SkyFoundry Find What Matters 4

5 Undersized VAV box, Sequence logic error, or High maximum airflow setpoint Info Gained: VAV box performance issues. Required data: Airflow rate error, Absolute value of airflow rate error, Temperature error, and Discharge air temperature error Energy Analysis Examples The report also describes a range of very useful energy- related analysis including: Base- to- Peak Load Comparison Description: Visualization with rule of thumb guidance on what to look for; and/or Calculate average base- to- peak load ratio over a given time period and provide interpretive guidance of result (e.g., closer to 1 is less desirable) Info Gained: Whether there is significant base load during unoccupied periods both during weekdays and weekends Required Data: Interval electric meter data (hourly or sub- hourly); and Building occupancy schedule Benchmarking Normalized Peak Electric Load Description: Compares peak load to rules of thumb benchmarks and provides interpretive guidance (e.g. 5 W/sf average based on CEUS data for selected small commercial types) Info Gained: Comparing power consumption per unit area (W/sf) with benchmarks and identifying potential reductions in demand charges and utility costs Required data: Interval electric meter data (hourly or sub- hourly), and Building area SkyFoundry Find What Matters 5

6 Note: This is accomplished in SkySpark in the Energy App by using the Normalize by Area options. Functions can also be written to perform these normalization and baseline comparison calculations to create rules that generate Sparks when desired ranges are exceeded. Carbon Accounting Description: Emissions calculated by carbon intensity per fuel (electricity or natural gas) use Info Gained: Building carbon footprint Step 1 Required data: Interval gas and electric meter data (hourly or sub- hourly), Building location information for utility generation look- up to determine Carbon content Full details on the workshop report can be found here: Source: U.S. Department of Energy Open Energy Information System (OpenEIS) Project: SkyFoundry Find What Matters 6

7 Summary of Outcomes of Workshop #1, OpenEIS Algorithms, held on February 26, 2013, March 14, 2013 Link to document: Workshop- 1- Summary pdf Going Deeper The reality is that all buildings and systems are different so predefined rules can only take us so far. That is why SkySpark is fully programmable to allow you as the systems expert to implement rules that identify operational issues that fit your specific application needs. SkyFoundry provides a wealth of documentation, training materials and even source code for rules to help you maximize the benefit of analytics for your projects. SkyFoundry Find What Matters 7

Persistence Tracking in a Retro-commissioning Program

Persistence Tracking in a Retro-commissioning Program Mike Eardley Architectural Energy Corporation Synopsis The persistence of energy savings and electrical demand reductions created by retrocommissioning