Comparison of CHEERS Energy Use predictions wt tb Actual Utility Bilk

Transcription

1 Comparison of CHEERS Energy Use Predictions with Actual Utility Bills Bruce A. Wilcox, P. E., Berkeley Solar Group, Piedmont, CA Marshall B. Hunt, P. E., Pacific Gas & Electric Company, San Francisco, CA Abstract The usefulness of Home Energy Rating Systems (HERS) is primarily derived from the accurate analysis of the present energy efficiency of a home and the cost effectiveness of the measures that are recommended for improving its efficiency. The Energy Efficient Mortgage is predicated on the concept that the mortgage money spent to improve the efficiency of a home will cost less per month to finance than the utility bill savings that are generated. Computer simulation programs are used to estimate the annual energy used for heating, cooling and domestic hot water. A large sample of rated homes in San Jose California was analyzed to compare predicted energy use with actual bills. The HERS predictions for both heating and cooling were found to significantly overestimate the energy use of low rated homes compared to efficient homes. Cooling energy use of low rated homes with air conditioning was actually lower than for efficient homes with air conditioning. Significant correlation between family characteristics and home efficiency are thought to be part of the reason for this dilemma. A number of areas are proposed for further work to improve the HERS estimates. Introduction Home Energy Rating Systems (HERS) providers offer a service, which rates the energy efficiency of a home. The rating is based on inspections and measurements of its energy efficiency features. The HERS rating includes an estimate of the average annual utility bills for the home and predictions of savings which will result from a series of recommended efficiency upgrades. Home buyers, utility programs, mortgage lenders and government agencies all depend on the accuracy of HERS ratings and utility bill estimates. In California the calculation technology has been based on California Energy Commission (CEC) procedures developed to show that new homes meet minimum energy codes requirements. While the scientific accuracy of the model is known, the accuracy of the predicted utility bills, particularly for older homes is questionable. For example, Berkeley Solar Group (BSG) studied new California homes built in 1987 in a variety of climates (Wilcox 199) and built in 1993 in hot valley climates (Wilcox 1996) and found that CEC calculations overstated cooling energy use. BSG made a number of suggestions to improve the accuracy of the compliance models (Lutz 1992), some of which were incorporated into the rules currently in effect. Jeff Stein compared CHEERS ratings with utility bills for two groups of houses, which included houses of various ages, and found significant discrepancies (Stein 1997). In this study we compare HERS ratings and predicted utility bills for a large sample of homes in San Jose California. This research is intended to support possible improvements in calculation assumptions to be adopted by the CEC in Methodology The CHEERS archive of data files for ratings performed in California is the basis for this study. This data includes the owner s name and address along with a complete description of the house; Comparison of CHEERS Energy Use predictions wt tb Actual Utility Bilk

2 all of its efficiency related components and the CHEERS rating and bills estimates. Using the owner information, Pacific Gas and Electric Company was able to provide 8 years of actual monthly gas and electric bills that provide the comparison with the CHEERS predictions. The sample was stratified by variables such as age, size, CHEERS rating, and family income, in order to determine whether these variables have an effect on the accuracy of the predictions. Each cell of the stratified sample will have approximately 1 homes to allow estimation of disaggregate average energy consumption for heating, cooling, water heating and miscellaneous electricity, with reasonable accuracy. The energy use pattern and the rating data of any individual house can not be extracted because they are part of the cell averages. The average CHEERS predictions in each cell were compared with the average billing estimate for that cell. Proposed modifications to the calculations will be executed on the houses in the database and the cell by cell comparison made again. The modifications will generic for the type of hourly, energy balance, thermodynamic simulation model that is used by CHEERS and by the CEC. Patterns of impacts will be develop and the set of modifications needed for some cells will be less than those needed for other cells. the proposed modifications will be presented in a later report. Utility Bill Analysis The utility billing data for PG&E consists of a series of usage amounts (therms of gas, kwh of electricity) with the date the meter was read. Meter readings occur approximately once per month, but the read date varies between houses and for the same house over time. The usage per day for each house for each bill was calculated. These values were then reallocated to calendar months for each house. Next, the average daily energy use for each house by month was calculated. The average annual usage is the sum of the average daily usage times the number of days in each month. The average annual usage is the sum of the average daily usage times the number of days in each month. The annual monthly energy usage for a group of homes is the average of the monthly average daily usage of each house in the group. The electricity and gas service provided to homes in this study is used for the full variety of appliances that exist in the homes. There are no homes for which electricity is used only for cooling and there are no homes for which gas is used only for heating. Estimating heating and cooling energy use from billing data requires that the billing consumption be allocated to the end uses in question, The approach taken is to use the seasonal pattern of billing consumption and assumptions about occupant behavior to estimate the fraction of the annual consumption attributable to space conditioning. California has cool winters and hot summers. It is reasonable to assume that occupants use little or no heating energy in the summer and little or no cooling energy in the winter. Since most of the homes in the sample used gas for primary heat and electricity for cooling, heating behavior is the most important factor in the seasonal use pattern for gas and cooling is most important factor in the seasonal use pattern for electricity Wilcox and Hunt

3 The approach taken was to identify the billing period in the summer when gas use is lowest and assign that amount of energy use to base non space heat end uses such as domestic water heating, cooking and clothes drying. Similarly, the lowest electricity use month in the winter was identified and assigned that level of use to refrigerator, TV, lighting and other electrical appliances. At the simplest level the energy attributable to heating in any other month is the bill for that month minus the base usage from the lowest usage month. The annual space heat is the sum of the heating energy for each month. A comparable analysis is done for cooling. Adjustments to the Gas Base The simplistic approach assigns all of the variation in gas use to heating and this ignores seasonal variation in other gas uses such as domestic water heating. In a previous study (Wilcox 199) analysis of a large set of sub-metered electric hot water heaters showed that the water heating energy was also seasonal. The water heating seasonality for that sample was correlated very well with the difference between the monthly mean outdoor temperature of the preceding month and the hot water set temperature. In equation form: DHWmonth = DHWmin x (DHWset-Tmonth)/(DHWset-Tmin) where: DHWmonth = domestic hot water energy for any month, DHWmin = domestic hot water energy in the lowest consumption month DHWset = water heater set temp, 14 for this study Tmonth = mean outdoor temperature for the preceding month Tmin = mean outdoor temperature in the month preceding the lowest consumption month This relationship can be used on a monthly basis, or for specific weather data sets annual factors can be developed. The month] y factors for CEC weather data for San Jose is shown in Table 1.. Table 1 Seasonal Base Gas Adjustment Month Note that this adjustment is relatively significant in mild California climates where domestic hot water heating is a large fraction of annual gas consumption. Zone 4 covers San Jose and the southern end of the San Francisco Bay. Zone 4 experiences the lowest base gas usage in September with a peak in February that is 127% higher. Other potentially seasonal gas usage Comparison of CHEERS Energy Use Predictions with Actual Utility Bills

4 factors include the impact of temperature and relative humidity on dryer energy use, the possible seasonal use of outdoor clotheslines, seasonal gas fireplace use, seasonal gas barbecue use etc. Because we have applied the DHW adjustment to the base gas usage, we have assumed that all non-heating gas uses vary with domestic hot water. This is reasonable since the other end uses are much smaller and difficult to separate from domestic hot water consumption, Gas Heating Disaggregation 4 I [ I 3.5 I I I I 1 I I I I * ~ E2 5.c * 1.5 Heating w Base Month Figure 1 San Jose Gas Heating Disaggregation Figure 1 shows the application of this approach to the gas usage of the San Jose houses (the 692 ho~ses without elect~{c heat, gas pool h~at or gas spa heat). The resulting estimate is 287 therms for heating and 252 therms of base usage. Adjustments to the electric base A similar adjustment is made to base electricity use to account for seasonal non-cooling energy variation. These variations are potentially from many sources such as number of daylight hours related to indoor and outdoor lighting energy use, variation in furnace fan usage, impact of ambient temperature on refrigerator operation, TV usage related to school and vacation schedules for families with children and seasonal weather impacts on indoor versus outdoor activities. For the San Jose sample we were able to estimate base electric usage using the 477 houses in the sample which had no air conditioning or electricity usage for pools or spas. The variation between the high of December to the low of May is 22% with the months of April through September being a steady 2% less than December Wilcox and Hunt

5 Table2 Month Seasonal Base Electric Adjustment Figure 2 shows the kwh per day for the 477 houses. The pattern was normalized multipliers for the usage in December as shown in Table 2. into monthly > 15. $ g Month Figure 2 San Jose Base Electric usage Electric Cooling Disaggregation The San Jose sample has 141 houses with air conditioning and no pool or spa electricity. The methodology used to establish monthly average base electrical usage for the 141 houses is shown in Figure 3 where the seasonal electricity usage is disaggregated for the San Jose cooling. The resulting estimate is 578 kwh for cooling and 6369 kwh for other uses. The mildness of the climate is shown by the fact that the winter usage is almost as high as the July and August usage. Future work with houses from the California central valley is expected to show a significantly higher level of air conditioning electrical usage. Comparison of CHEERS Energy Use Predictions with Actual Utility Bills

6 25 2 ~Cooling pigbase 5 I Month Figure3 San Jose Cooling Disaggregation The San Jose Sample Initial analysis focuses on approximately 1 CHEERS rated homes that were participants in the pilot project carried out by PG&E in San Jose, California in CHEERS provided owner data. Non-single family units and builder models were eliminated. PG&E identified as many customers as possible and supplied billing data for January 1989 through September Ultimately, a total of 788 homes with usable data were assembled. All homes have at least 3 years of data and about 9% have the complete 68 month record. The CHEERS Version 1 rating tool was used to generate ratings and annual energy use predictions. Characteristics of San Jose Homes The distribution of year built for the San Jose houses is shown in Figure 4. Less than 1% of the homes were built after the California Building Energy Standards went into effect. Almost 25% of the homes are more than 4 years old. The average year that the houses in the sample were built was The distribution of house size is shown in Figure 5 for the San Jose Sample. The average size is x square feet and the median house size is 15 square feet. The total number of large houses over 25 square feet is xxx Wilcox and Hunt

7 Distribution of Year Constructed San Jose (n=788) 22 I L U g 194 m 192 $ Cumulative Number of Homes Figure 4 Distribution of San Jose Year of Construction Distribution of Floor Area, San Jose (rt=788) l _._+ ~ 3 : 25 IA Cumulative Number of Homes I Figure 5 Distribution of San Jose Conditioned Floor Area Comparison of CHEERS Energy Use Predictions with Actual Utility Bills

8 Table 3 San Jose Heating and Air Conditioning Characteristics HeatingEquipment Houses CoolingEquipment Houses BoilerGas, AFUE_O.55 1 Air Cond, Central,SEER_6, 3 BoilerGas, AFUE_O.7 4 Air Cond, Central,SEER_7.O 84 BoilerGas, AFUE_O.75 1 Air Cond. Central,SEER_8.O 32 ElectricBaseboard 7 Air Cond. Central,SEER_9.O 11 FurnaceElectric 3 Air Cond. Central,SEER_l. 15 FurnaceGas, AFUE_O.55 4 Air Cond. Central,SEER_l 1. 2 FurnaceGas, AFUE_O.6 6 Air Cond, Central,SEER_l2. 1 FurnaceGas, AFUE_O Air Cond, Central,SEER_l3. 1 FurnaceGas, AFUE_O Air Cond, Room,SEER_6.O 1 FurnaceGas, AFUE_O Air Cond, Room,SEER_7.O 45 FurnaceGas, AFUE_O.8 5 AirCond. Room,SEER_8.O 1 FurnaceGas, AFUE_O Air Cond, Room,SEER_9.O 2 FurnaceGas, AFUE_O.9 3 Air Cond. Room,SEER_1.O 1 FurnaceGas, AFUE_O.95 1 DirectEvapCooler 3 Water HeaterGas, RE_O.75 1 None 596 GrandTotal 798 GrandTotal 798 Table 3 lists the heating and cooling systems in the San Jose homes using their CHEERS designations. All but 17 of the homes have gas heat (the definition includes wall, floor and space heaters). A full 98% of the homes have central forced air gas heating. About 25% of the homes have air conditioning with 25% of those being room air conditioners. Table 4 San Jose Pools and Spas Electricity for Electricity for Gas for Gas for Spa No Poolor Pool Spa Pool Spa Houses Table 4 lists the pools and spas located at the San Jose homes. Twenty-one percent(21 %) of the homes have a pool, spa or both. Pools and Spas can use significant amounts of gas and electricity that will effect the overall bills and HERS estimates. Pool and spa use is also likely to be seasonal so these houses must be removed from the study sample to allow the seasonal heating and cooling bill disaggregation. San Jose Weather The weather during the billing data period affected the billing energy used for heating and cooling and if it was different than the weather on the CEC Climate Zone 4 weather file would cause differences between the CHEERS predictions and the bills. Figure 6 shows the heating degree days base 65 F for the study years, the CEC weather file for CTZ 4 and the long term average. The CTZ 4 weather has 18% more heating degree days than the average during the study period. It is reasonable to multiply the CHEERS heating results by.85 to make them comparable to the billing data Wilcox and Hunt

9 Heating Degree Day Comparison, San Jose 3 25 r I ~ ~1993,1994 ~1995~ ;79-, CTZ4 1~78- ihdd 2267 ~2322 ~ I 287 ~237 I I 243 I 2414 I 2331 Period Figure 6 Comparison of Heating Degree Days Figure 7 shows the Cooling Degree Days base 65 during the study period, the CTZ 4 weather file and the long term record. Again, the CHEERS cooling results can be multiplied by.88 to adjust for the differences between the weather file and the climate during the billing period. Cooling Degree Day Comparison, San Jose 12 1 E 8 m W 6 ~ n ~ ~ ~ I 1995 I 1996 ~ ; I 676 ~ 83 I ~ HDD 63 ~ 74 : 61 Period 1:79-~ CTZ 4 : I Figure 7 Comparison of Cooling Degree Days Compart son of CHEERS Energy Use Predictions with Actual Utility Bills

10 Comparison of Bills with CHEERS Predictions Figure 8 and 9 answer the fundamental questions about the accuracy of the CHEERS Version 1 tool and rating process. Does the tool accurately predict which houses use more energy and does it accurately predict how much more energy the low rated houses use? Gas Heat from Bills and CHEERS Predictions Grouped by CHEERS Heating Score San Jose Sample, No Pools or Spas (n=692) I Average Group Heating Score Figure 8 Heating from Bills Compared with CHEERS Predictions Figure 8 shows the billing derived heating energy use and the comparable CHEERS predictions of heating energy use for houses with a range of heating scores. The 692 houses with simple gas usage (no pools or spas using gas heat) are divided into 1 groups of approximately 69 houses each according to their CHEERS heating score. The lowest rated group, with an average CHEERS heating score of 53, has the least efficiency measures and should use the most gas heat. The highest rated group with an average heating score of 8 should use the least heating energy. The bar on the left for each group is the average heating gas consumption of the houses in the group derived from bills. The bar on the right for each group is the average CHEERS predicted heating gas usage for the houses in the group. The answer to the first question is that yes the CHEERS tool does predict which houses use more energy. Houses in the lowest rated groups use an average of 345 therms per year while the houses in the highest rated group use an average of 249. The actual energy use gradually declines as the group rating score increases. There area couple of groups that don t follow the Wilcox and Hunt

11 smooth trend, possibly because the houses may not be of the same size or there are occupancy differences between groups. However, the CHEERS Version 1 tool clearly does not accurately predict the difference in energy use between houses with high and low ratings. CHEERS predicts that the lowest rated houses will use an average of 1116 therms per year compared to its predicted 296 for the highest rated houses. This is a ratio of almost 4 to 1 in heating energy use. The savings for choosing a high rated house versus a low rated house according to CHEERS is over 8 therms per year. In fact the lowest rated houses use only 96 therms more than the highest rated houses, a difference of about 4%. The CHEERS estimate of differences between the houses is off by a factor of 8. Electric Cooling Grouped by CHEERS Cooling Score San Jose Houses with Cooling (n=154) Bills CHEERS I I 72 ~ 81 ~ S8Bills ~ 472 i 483 ~ IW CHEERS 1536 I_ 82! ~.-. Average, Group Cooling Score Figure 9 Cooling from Bills Compared with CHEERS Predictions Figure 9 shows the same comparison for cooling electricity usage derived from bills and CHEERS version 1 predictions. Unfortunately, only 154 houses in the San Jose Sample have air conditioning and no pools or spas to confound the billing energy analysis. The sample was grouped according to CHEERS cooling rating into 4 groups of about 38 houses each. The answers to the fundamental accuracy questions are not as good for cooling. The ratings do not predict which houses use more cooling energy. The highest rated houses use almost 5% more than the lowest rated houses. Comparison of CHEERS Energy Use Predictions with Actual Utility Bills

12 Smoothed Total Gas Usage by Heat Score Group San Jose Sample (N=788) Heat Score <6 : 1.2 -L All 1 u, ~ Heat Score >78 ~.8 ~_ Houses > 78/ Houses <6 & Year Figure 1 Heating Energy Use Over the Billing Period The obvious question is why are the predictions wrong. One theory is that the rating system worked so well that the owners of the low rated houses all had their houses upgraded after the HERS ratings. Unfortunately, Figure 1 shows that the heating energy use of the homes in the lowest and highest rated groups maintained the same ratio throughout the analysis period. This means that no large improvements were made in the low rated group Wilcox and Hunt

13 Gas Heat from Bills and CHEERS Predictions Grouped by Household Income San Jose Sample, No Pools or Spas (n=692) 9 I I [ _ Bills - OCHEERS Linear Linear (CHEERS (Bills) Average Household Income ($1) Figure 11 Heating Energy Use Versus Household Income Figure 11 shows that family income, a characteristic which does not enter into the rating of the house seems to be related to heating energy use. US Census data for San Jose zip codes was used to estimate the household income based on the location of the house. The CHEERS predictions show that energy use should go down slightly as income goes up. This is probably due to a tendency for higher income families to live in newer more highly rated homes. The actual heating energy trend is significantly the opposite, with families in wealthier neighborhoods using more than 5% more heating energy The impact of occupant behavior It is no surprise that occupant behavior has a significant impact on household energy use. A landmark study of nearly identical townhouses 2 years ago showed that factor of 2 differences in energy use were caused by the occupants not the efficiency of the buildings (Sonderreger 1978). Previous work in California houses has shown that home occupants have significantly different strategies and patterns aof thermostat behavior. A study of low income families in 1 nearly identical Florida homes showed that cooling energy use variation of 4 to 1 could be attributed to thermostat behavior and appliance heat gains (Parker 1996). If families living in low rated houses were systematically different from families living in high rated houses then the high rated houses could use more energy regardless of their efficiency. It seems obvious that this could be the case. Comparison of CHEERS Enetgy Use Predictions wz tb Actual Utility Bilk

14 Simulation Calibration Issues to Achieve Accuracy The following issues have been identified for further work in subsequent phases of the project. The goal of this effort will be to recommend technical improvements in CHEERS and other HERS rating tools. The final outcome can not be known until the data is produced and analyzed, but it is clear that there is no simple answer such as arbitrarily reducing the energy use prediction by a fixed multiplier. We expect the following issues will be significant: 1. The CHEERS Version 1 tool assumes (according to CEC rules) that a house without a setback thermostat is always conditioned instead of having a significant setback at night. New high rated houses all have setback thermostats while most older homes do not. Previous field research (Wilcox 199) has shown that most California families do not use their programmable thermostats and average setbacks are very small. 2. The simulation assumes that a comfort is achieved in the winter with a setting of 7 degrees and in the summer with a setting of 78 degrees. The San Jose weather rarely gets to below freezing and many central forced air systems have heating capacities far in excess of what is needed. This allows occupants to use the thermostat like a light switch, turning on heat when they want heat and turning it off when they are not at home or have gone to bed. Such a strategy is not possible in severe winter climates where there is the risk of frozen pipes. 3. The CHEERS Version 2 tool uses an improved computer model which would more accurately calculate heating and cooling energy for any set of assumptions because of its improved algorithms for heat transfer through windows. This tool has been developed to have better technical and scientific accuracy and should provide better predictions. The Central Valley sample of CHEERS predictions would provide an indication of whether improved simulation algorithms can improve the accuracy of the predictions. 4. Infiltration is the most significant heating load and maybe overestimated particularly in older, lower rated homes. No measurements were made of the infiltration rates of the homes that were rated. 5. The performance of old uninsulated walls, ceilings, and windows maybe biased too low by the CEC rules. In addition, the performance of insulated walls, ceilings, and windows may not be as good as standards engineering calculations would predict. Taken together, these factors bring the energy use of the two ends of the energy efficiency spectrum toward each other. In old insulated homes there can be debris, dust, dirt and construction techniques that result in a higher resistance to heat flow than predicted. Old single pane windows that are well shaded and draped can have thermal performance that is not poor as predicted. Studies have found that the solid wood framing in stud walls is more than 3% instead of the 25% that is assumed by the CEC rules n(rainer, 1995). Surveys of insulated homes have found a number of insulation short circuits and voids. 6. The performance of older forced air systems maybe biased too low by not accounting for the evolution of duct systems. A well made older duct system can have much less leakage than a new duct system that has been put together poorly using materials that have failed Wilcox and Hunt

15 References Lutz, J Simulation Software Gets Reality Check, Home Energy, September/October, Lutz, J. and Wilcox, B. A., 199. Comparison of Self-Reported and Measured Thermostat Behavior in New California Houses, In Proceedings of the 199 Summer Study on Energy Efficiency in Building, Vol. 2, p , American Council for an Energy Efficient Economy, Washington, DC. Parker et al., 1996, Central AC Usage Patterns in Low Income Housing in a Hot and Humid Climate In Proceedings of the 1996 Summer Study on Energy Efficiency in Building, American Council for an Energy Efficient Economy, Washington, DC. Rainer, L. Is an R- 19 Wall Really R- 19? Home Energy, March/April Sonderegger, R.C Movers and Stayers: The Resident s Contribution to Variation Across Houses in Energy Consumption for Space Heating, Energy and Buildings, Vol. 1, No. 3, Elsevier Sequoia, Netherlands. Stein, Jeffery, Home Energy Rating Systems, Actual Usage May Vary, Home Energy, September/October Wilcox, B.A. Occupancy Patterns and Energy Consumption in New California Houses ( ), California Energy Commission, P4-9-9, 199. Wilcox, B. A. Energy Use of California 1993 Title 24 Houses Berkeley Solar Group, Oakland, CA Comparison of CHEERS Enetgy Use Predictions w th Actual Utility Bills

16