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Comparing Coincidence Factors in Residential and Commercial Lighting, Lecture notes of Energy Efficiency

An in-depth analysis of the Seasonal Peak and On-Peak Coincidence Factors for Residential and Commercial & Industrial Lighting. The study compares the performance of logger data sources, sector types, and occupancy sensors during Summer and Winter seasons. The results show significant differences between Summer On-Peak and Seasonal Peak CFs, as well as between Large and Small C&I Lighting. The document also includes interactive tables and charts for a better understanding of the data.

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Coincidence Factor Study
Residential and Commercial Industrial Lighting Measures
Prepared for;
New England State Program Working
Group (SPWG)
For use as an
Energy Efficiency Measures/Programs
Reference Document for the
ISO Forward Capacity Market (FCM)
Spring 2007
Prepared for:
New England State
Program Working Group (SPWG)
Prepared by:
179 Main Street
Middletown, CT 06457
(860) 346-5001
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Download Comparing Coincidence Factors in Residential and Commercial Lighting and more Lecture notes Energy Efficiency in PDF only on Docsity!

Coincidence Factor Study Residential and Commercial Industrial Lighting Measures

Prepared for;

New England State Program Working

Group (SPWG)

For use as an

Energy Efficiency Measures/Programs

Reference Document for the

ISO Forward Capacity Market (FCM)

Spring 2007

Prepared for:

New England State

Program Working Group (SPWG)

Prepared by:

179 Main Street

Middletown, CT 06457

Acknowledgement:

The authors wish to thank all of the people at the State Program Working Group (SPWG), Northeast Energy Efficiency Partnership (NEEP) who took the time to support and help with this study. Regrettably, we cannot thank everyone individually, but we do want to acknowledge the contributions made by Julie Michaels, Jeff Schlegel Tom Belair, Carol White, Chris Neme and Ralph Prahl. The data, insight, and support provided by these individuals helped to establish the foundation for this report. RLW assumes sole responsibility for any errors or omissions in this report.

Index of Tables

  • 8.2 Comparison of C&I Lighting Profiles..........................................................................
  • 8.3 Large and Small C&I Lighting Coincidence Factors...................................................
  • 8.4 On-Peak Large and Small C&I Lighting Coincidence Factors....................................
  • 8.5 Seasonal Peak Large and Small C&I Lighting Coincidence Factors...........................
  • 8.6 Comparison of On-Peak and Seasonal Peak CFs for C&I Lighting
  • 8.7 Commercial & Industrial Interactive Demand Coincidence Factors
  • 8.8 Combined C&I Lighting and Interactive Demand Coincidence Factors
  • APPENDIX A – DATA S OURCES
    • Residential Lighting Logger Data Sources................................................................................
    • Commercial & Industrial Lighting Logger Data Sources
  • Table 1: Seasonal Peak Forecasts....................................................................................................
  • Table 2: Analysis of Post SMD Critical Peak Performance Hours
  • Table 3: Residential Logger Data....................................................................................................
  • Table 4: Seasonal Residential Logger Data.....................................................................................
  • Table 5: Summer On-Peak CFs and Relative Precisions Residential Lighting.............................
  • Table 6: Winter On-Peak CFs and Relative Precisions Residential Lighting
  • Table 7: Summer Seasonal Peak Coincidence Factors Residential Lighting
  • Table 8: Winter Seasonal Peak Coincidence Factors Residential Lighting
  • Table 9: Comparison of Summer On-Peak and Seasonal Peak CFs Residential Lighting............
  • Table 10: Comparison of Winter On-Peak and Seasonal Peak CFs Residential Lighting
  • Table 11: C&I Lighting Logger Data
  • Table 12: Summer On-Peak Coincidence Factors C&I Lighting.................................................
  • Table 13: Winter On-Peak Coincidence Factors C&I Lighting
  • Table 14: Summer Seasonal Peak Coincidence Factors C&I Lighting.........................................
  • Table 15: Winter Seasonal Peak Coincidence Factors C&I Lighting
  • Table 16: Comparison of Summer On-Peak and Seasonal Peak CFs C&I Lighting.....................
  • Table 17: Comparison of Winter On-Peak and Seasonal Peak CFs C&I Lighting
  • Table 18: Summer On-Peak Coincidence Factors C&I Occupancy Sensors
  • Table 19: Winter On-Peak Coincidence Factors C&I Occupancy Sensors..................................
  • Table 20: Summer Seasonal Peak Coincidence Factors C&I Occupancy Sensors
  • Table 21: Winter Seasonal Peak Coincidence Factors C&I Occupancy Sensors..........................
  • Table 22: Comparison of Summer On-Peak and Seasonal Peak CFs Occupancy Sensors
  • Table 23: Comparison of Winter On-Peak and Seasonal Peak CFs Occupancy Sensors..............
  • Table 24: C&I Lighting Data Sources...........................................................................................
  • Table 25: Summer On-Peak Coincidence Factors Large and Small C&I Lighting......................
  • Table 26: Winter On-Peak Coincidence Factors Large and Small C&I Lighting
  • Table 27: Summer Seasonal Peak Coincidence Factors Large and Small C&I Lighting..............
  • Table 28: Winter Seasonal Peak Coincidence Factors Large and Small C&I Lighting
  • Table 29: Comparison of Summer On-Peak and Seasonal Peak CFs C&I Lighting.....................
  • Table 30: Comparison of Winter On-Peak and Seasonal Peak CFs C&I Lighting
  • Table 31: Interactive Summer On-Peak Coincidence Factors C&I Lighting...............................
  • Table 32: Interactive Winter On-Peak Coincidence Factors C&I Lighting
  • Table 33: Interactive Summer Seasonal Peak Coincidence Factors C&I Lighting......................
  • Table 34: Interactive Winter Seasonal Peak Coincidence Factors C&I Lighting
  • Table 35: Combined Summer On-Peak Coincidence Factors C&I Lighting
  • Table 36: Combined Winter On-Peak Coincidence Factors C&I Lighting..................................
  • Table 37: Combined Summer Seasonal Peak Coincidence Factors C&I Lighting
  • Table 38: Combined Winter Seasonal Peak Coincidence Factors C&I Lighting.........................
  • Figure 1: Distribution of Summer Seasonal Peak Hours................................................................. Index of Figures
  • Figure 2: Distribution of Winter Seasonal Peak Hours
  • Figure 3: Frequency of OP 4 Events by Year..................................................................................
  • Figure 4: Comparison of Un-weighted and Weighted Summer Lighting Profiles
  • Figure 5: Comparison of Un-weighted and Weighted Winter Lighting Profiles
  • Figure 6: C&I Profiles for Non-Occupancy Sensor Lighting.......................................................
  • Figure 7: C&I Profiles for Occupancy Sensor Controlled Lighting..............................................
  • Figure 8: Testing and Replacement of Lighting Logger Battery...................................................
  • Figure 9: Lighting Logger Installation and Calibration................................................................
  • Figure 10: Comparison of Large C&I and Small C&I Lighting Profiles

2007 Coincidence Factor Study_________________________________________Page II

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  • Critical Peak Performance Hours occur when the Day Ahead Load forecast is equal to or greater than 95% of the 50/50 seasonal peak load forecast during Summer (June – August) or Winter (December and January) months and also includes shortage hours. ¾ Shortage hours occur during Operating Procedure 4 (OP4) level 6 or higher events, at level 6 the 30-minute operating reserve begins to be depleted.

Coincidence Factors (CFs) are defined in this study as the fractions of the connected (or rated) load (based on actual lighting Watts, motor nameplate horsepower and efficiency, AC rated capacity and efficiency, etc.) reductions that actually occur during each of the seasonal demand windows. They are the ratio of the actual demand reductions during the coincident windows to the maximum connected load reductions. Under this definition other issues such as diversity and load factor are automatically accounted for, and only the coincidence factor will be necessary to determine coincident demand reductions from readily observable equipment nameplate (rated) information. In other words, coincident demand reduction will simply be the product of the coincidence factor and the connected equipment load kW reduction.

Residential Lighting Coincidence Factor Results

Table i - 1 and Table i - 2 provide the un-weighted and weighted, Summer On-Peak and Winter On-Peak CFs as well as the associated relative precisions for all residential lighting. The CFs were developed using only metered data that were acquired during the winter (December and January) or summer (June, July and August) peak months and the number of loggers used in the analysis is provided in the tables. The weighted CFs were developed by weighting the logger files based upon the connected load that the logger represents and in most cases the weighted results are slightly higher than the un-weighted results. The CFs for the summer range from a low of 0.06 for June to a high of 0.094 for August, with the average summer CF between 0. un-weighted and 0.082 weighted. If the average is carried to only two decimal places than the result is a summer average CF of 0.08 for both methodologies. The relative precision for the average summer on-peak period is ±6.1% at the 80% confidence interval.

2007 Coincidence Factor Study________________________________________ Page III

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Sample Size Un-weighted Weighted Un-weighted Data Period n CF CF Rel Precision June 210 0.060 0.069 ±11.6% July 102 0.081 0.086 ±12.5% August 189 0.094 0.092 ±8.7% Average Summer 501 0.076 0.082 ±6.1%

Summer On-Peak Hours 1PM - 5PM

Table i - 1: Summer On-Peak CFs and Relative Precisions Residential Lighting

Sample Size Un-weighted Weighted Un-weighted Data Period n CF CF Rel Precision December 282 0.263 0.281 ±6.5% January 264 0.301 0.320 ±6.5% Average Winter 546 0.286 0.298 ±4.5%

Winter On-Peak Hours 5PM - 7PM

Table i - 2: Winter On-Peak CFs and Relative Precisions Residential Lighting The winter CFs as expected are higher than the summer CFs ranging from 0.263 for December to 0.320 for January with the average winter CF for all lighting at 0.286 un-weighted and 0. weighted. The relative precisions is better during the winter peak periods primarily because the CFs are higher and there is less variation in the data, i.e. the Coefficient of Variation (Cv) is lower. The relative precision of the average winter un-weighted CF is ±4.5% at the 80% confidence interval and the December and January relative precisions are both better than ±10% at the 80% confidence interval.

The Seasonal Summer and Winter Peak performance hours were calculated using historical load data and the 50/50 Seasonal Peak Forecasts from the most recent Capacity Energy Loads and Transmission (CELT) reports. The seasonal peak performance hours were weighted based upon the frequency distribution of the hours observed where the load met or exceeded 90% of the 50/50 seasonal peak forecast and these values were used to calculate a weighted CF for each of the measure types. Table i - 3 and Table i - 4 provide the Summer Seasonal Peak and Winter Seasonal Peak CFs for all residential lighting. The CFs during the summer months range from a low of about 0.08 for June to a high of 0.10 for August, with an Average Summer CF of about 0.09. The relative precision during each of the summer months is within the range of ±10% at the 80% confidence interval. The Winter Seasonal Peak CFs as expected, are higher than the Summer Seasonal Peak CFs ranging from 0.25 in December to 0.28 in January with an Average Winter Seasonal Peak CF for all lighting at 0.26.

2007 Coincidence Factor Study_________________________________________ Page V

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On-Peak Seasonal % Change Un-weighted Un-weighted Seasonal/ Data Period CF CF On-Peak December 0.263 0.249 95% January 0.301 0.279 93% Average Winter 0.286 0.264 92% Table i - 6: Comparison of Winter On-Peak and Seasonal Peak CFs Residential Lighting

Commercial & Industrial Lighting Coincidence Factor Results

A similar Coincidence Factor analysis was also conducted for Commercial and Industrial Lighting and Occupancy Sensor measures. The logger data were analyzed by sector so that results could be applied to multiple programs with different participation rates among the different sectors. Table i - 7and Table i - 8 provide the On-Peak CFs for the ten C&I sectors along with the associated relative precisions and total estimated CFs based on a logger weighted strategy and weighting each sector equally. The Summer On-Peak CFs indicates that the Grocery sector has the highest CF of about 0.95, while the Other sector has the lowest CF of about 0.54. All of the sectors have relative precisions that are within ± 5% at the 80% confidence interval. The Grocery sector also had the highest Winter On-Peak CF of about 0.78, while the School sector had the lowest CF of about 0.34. Once again the relative precisions were all quite good with each sector exceeding ± 10% at the 80% confidence interval. As expected the Winter On- Peak CFs were lower than the Summer On-Peak CFs for all of the C&I lighting sectors, because the performance hours occur later in the day as C&I facilities are shutting down and lighting is being switched off.

2007 Coincidence Factor Study________________________________________ Page VI

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Sample Size Calculated Logger Calculated Calculated Sector Type n CF Weight CV Rel Precision Grocery 37 0.948 0.026 0.179 ±1.9% Manufacturing 169 0.729 0.119 0.488 ±2.4% Medical (Hospital) 58 0.769 0.041 0.425 ±3.6% Office 259 0.750 0.183 0.438 ±1.7% Other 192 0.543 0.136 0.675 ±3.1% Restaurant 43 0.811 0.030 0.347 ±3.4% Retail 166 0.824 0.117 0.342 ±1.7% University/College 70 0.680 0.049 0.483 ±3.7% Warehouse 59 0.781 0.042 0.359 ±3.0% School 362 0.633 0.256 0.503 ±1.7% 0.704 1.

Summer On-Peak Hours 1PM - 5PM

Total Weighted by Logger Total Equal Weight by Sector Table i - 7: Summer On-Peak CFs and Relative Precision C&I Lighting

Sample Size Calculated Logger Calculated Calculated Sector Type n CF Weight CV Rel Precision Grocery 37 0.776 0.026 0.474 ±7.1% Manufacturing 169 0.399 0.119 0.983 ±6.9% Medical (Hospital) 58 0.603 0.041 0.593 ±7.1% Office 259 0.537 0.183 0.725 ±4.1% Other 192 0.426 0.136 0.804 ±5.3% Restaurant 43 0.663 0.030 0.557 ±7.7% Retail 166 0.655 0.117 0.592 ±4.2% University/College 70 0.523 0.049 0.679 ±7.4% Warehouse 59 0.496 0.042 0.787 ±9.3% School 362 0.343 0.256 1.010 ±4.8% 0.480 1.

Winter On-Peak Hours 5PM - 7PM

Total Weighted by Logger Total Equal Weight by Sector Table i - 8: Winter On-Peak CFs and Relative Precision C&I Lighting

Table i - 9and Table i - 10 provide the Summer and Winter Seasonal-Peak CFs for the ten C&I sectors along with the associated relative precisions and total estimated CFs based on a logger weighted strategy and weighting each sector equally (which is the simple average of the CFs across all sectors. The Seasonal Peak Performance Hours were determined by analysis of historic ISO-NE Load Data and Forecast Data to determine the frequency distribution for each hour where the demand was greater than or equal to 90% of the seasonal forecast. A simple probabilistic weighting scheme was applied based upon the number of observation during each hour as described in section

2007 Coincidence Factor Study_______________________________________ Page VIII

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Sample Size Calculated Logger Calculated Calculated Sector Type n CF Weight CV Rel Precision Grocery 37 0.770 0.026 0.44 ±4.6% Manufacturing 169 0.432 0.119 0.91 ±4.2% Medical (Hospital) 58 0.618 0.041 0.58 ±4.5% Office 259 0.539 0.183 0.71 ±2.6% Other 192 0.428 0.136 0.80 ±4.4% Restaurant 43 0.644 0.030 0.59 ±5.3% Retail 166 0.647 0.117 0.59 ±2.7% University/College 70 0.528 0.049 0.60 ±4.2% Warehouse 59 0.535 0.042 0.70 ±5.6% School 362 0.388 0.256 0.85 ±2.7% 0.497 1.

Winter Seasonal Peak Hours (90% of 50/50 Peak)

Total Weighted by Logger Total Equal Weight by Sector Table i - 10: Winter Seasonal Peak CFs and Relative Precision C&I Lighting Table i - 11 provides a comparison of the Summer On-Peak and Seasonal Peak CFs for each of the C&I sectors, which shows that for every sector the Summer Seasonal CFs are lower than the Summer On-Peak CFs. This means that if the C&I lighting were classified as Summer Seasonal Peak assets the demand reductions would be lower.

% Change

On-Peak Seasonal Seasonal /

Sector Type CF CF On-Peak

Grocery 0.948 0.904 95%

Manufacturing 0.729 0.671 92%

Medical (Hospital) 0.769 0.740 96%

Office 0.750 0.702 94%

Other 0.543 0.476 88%

Restaurant 0.811 0.775 96%

Retail 0.824 0.795 96%

University/College 0.680 0.650 96%

Warehouse 0.781 0.727 93%

School 0.633 0.599 95%

Total Weighted by Logger 0.704 0.660 94%

Total Equal Weight by Sector 0.747 0.704 94%

Summer

Table i - 11: Comparison of Summer On-Peak and Seasonal Peak CFs C&I Lighting

Table i - 12 provides a similar comparison of the Winter On-Peak and Seasonal Peak CFs for each of the C&I Lighting sectors. In this case the results are mixed, with 7 of the 10 sectors showing an

2007 Coincidence Factor Study________________________________________ Page IX

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increase in the Winter Seasonal Peak CFs compared to the Winter On-Peak CF. This seems to indicate that in general for the winter, C&I lighting would have more demand reduction if classified as a Seasonal Peak asset.

% Change

On-Peak Seasonal Seasonal /

Sector Type CF CF On-Peak

Grocery 0.776 0.770 99%

Manufacturing 0.399 0.432 108%

Medical (Hospital) 0.603 0.618 103%

Office 0.537 0.539 101%

Other 0.426 0.428 100%

Restaurant 0.663 0.644 97%

Retail 0.655 0.647 99%

University/College 0.523 0.528 101%

Warehouse 0.496 0.535 108%

School 0.343 0.388 113%

Total Weighted by Logger 0.480 0.497 104%

Total Equal Weight by Sector 0.542 0.553 102%

Winter

Table i - 12: Comparison of Winter On-Peak and Seasonal Peak CFs C&I Lighting

Commercial & Industrial Occupancy Sensor Coincidence Factor Results

Table i - 13 and Table i - 14 present the Summer On-Peak and Winter On-Peak CFs for occupancy sensors for seven of the ten C&I sectors as well as the total CFs for all seven sectors on a logger weighted basis and by weighting each sector equally. During the Summer On-Peak Period the occupancy sensors installed in the University/College sector had the highest CF of about 0.30, while the Other sector had the lowest CF of about 0.02. The Summer On-Peak CF for the remaining sectors ranged from about 0.21 for Manufacturing to 0.27 for the Office Sector. During the Winter On-Peak the Office sector had the highest CF of about 0.31 and the Other sector had the lowest CF of 0.09. The CFs for the remaining sectors ranged from a low of about 0.17 for the Warehouse sector to a high of about 0.23 for the University/College sector. The relative precision for all of the CFs were estimated by calculating the relative precision of the occupancy sensors profiles, since only aggregate savings profiles were developed for the analysis. In this case we would recommend using the logger weighted Total CFs since the relative precision for individual sector results are not that good particularly during the Winter period.

2007 Coincidence Factor Study________________________________________ Page XI

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Sample Size Calculated Logger Estimated Estimated Data Period n CF Weight CV Rel Precision Manufacturing 12 0.198 0.035 0.712 ±8.9% Medical 59 0.239 0.170 0.649 ±3.6% Office 69 0.274 0.199 0.606 ±3.2% Other 56 0.024 0.161 0.808 ±4.6% University/College 16 0.283 0.046 0.720 ±7.6% Warehouse 77 0.246 0.222 0.700 ±3.3% School 58 0.209 0.167 0.739 ±4.2% 0.208 1.

Total Weighted by Logger Total Equal Weight by Sector

Summer Seasonal Peak Hours (90% of 50/50 Peak)

Table i - 15: Summer Seasonal-Peak CFs and Relative Precision C&I Occupancy Sensors

Sample Size Calculated Logger Estimated Estimated Data Period n CF Weight CV Rel Precision Manufacturing 12 0.172 0.035 1.063 ±17.3% Medical 59 0.221 0.170 0.827 ±6.3% Office 69 0.296 0.199 0.966 ±6.9% Other 56 0.066 0.161 0.990 ±7.7% University/College 16 0.231 0.046 0.819 ±11.9% Warehouse 77 0.183 0.222 0.986 ±6.6% School 58 0.159 0.167 1.140 ±8.7% 0.191 1.

Total Weighted by Logger Total Equal Weight by Sector

Winter Seasonal Peak Hours (90% of 50/50 Peak)

Table i - 16: Winter Seasonal-Peak CFs and Relative Precision C&I Occupancy Sensors

Table i - 17 and Table i - 18 provide a comparison of the Summer and Winter On-Peak and Seasonal Peak CFs for occupancy sensors for seven C&I sectors as well as the totals for all seven sectors calculated on a logger weighted and sector weighted basis. The results for the Summer period show that the Summer Seasonal CFs are lower than the On-Peak CFs for four of the seven sectors and for the total CF using both calculation methods. The results for the Winter period are similar, with five of the sectors having lower Seasonal Peak CFs and lower Total CFs using both calculation methods. Classifying the occupancy sensors as Seasonal Peak assets would result in a slight reduction in demand savings during both periods.

2007 Coincidence Factor Study________________________________________Page XII

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% Change

On-Peak Seasonal Seasonal /

Sector Type CF CF On-Peak

Manufacturing 0.210 0.198 94%

Medical 0.234 0.239 102%

Office 0.270 0.274 101%

Other 0.017 0.024 144%

University/College 0.304 0.283 93%

Warehouse 0.266 0.246 92%

School 0.239 0.209 87%

Total Weighted by Logger 0.217 0.208 96%

Total Equal Weight by Sector 0.154 0.147 96%

Summer

Table i - 17: Comparison of Summer On-Peak and Seasonal Peak CFs Occupancy Sensors

% Change

On-Peak Seasonal Seasonal /

Sector Type CF CF On-Peak

Manufacturing 0.190 0.172 90%

Medical 0.213 0.221 104%

Office 0.309 0.296 96%

Other 0.089 0.066 75%

University/College 0.233 0.231 99%

Warehouse 0.175 0.183 105%

School 0.173 0.159 92%

Total Weighted by Logger 0.197 0.191 97%

Total Equal Weight by Sector 0.138 0.133 96%

Winter

Table i - 18: Comparison of Winter On-Peak and Seasonal Peak CFs Occupancy Sensors

Program Level C&I Lighting CF Calculations

Several of the study sponsors wanted to calculate the C&I Lighting CFs using an alternative method that grouped the logger data into two categories, Large C&I and Small C&I since this provides them with results that are more in-line with their tracking systems, which track results at the program level. The sponsors also wanted to estimate the electrical demand impacts attributable to the interaction between the lighting and the HVAC systems and those results are presented in the following sections as well. The new C&I lighting CFs and interactive effects were only developed for C&I lighting measures, occupancy sensor measures were not included as part of the analysis.

2007 Coincidence Factor Study_______________________________________ Page XIV

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for both the Large and Small C&I CFs are better than ± 3% at the 80% confidence interval. For the Winter, the results are similar to the On-Peak results however the Seasonal Peak values are slightly higher for both Large and Small C&I lighting. This is due to the fact that there are more morning and afternoon hours included in the CF calculation where the lighting operates at a higher percent on. Once again note that the relative precision for each of the C&I sector CFs is better than ± 5% at the 80% confidence interval.

Sample Size Calculated Calculated Calculated Program Type n CF CV Rel Precision Large C&I 408 0.714 0.416 ±2.6% Small C&I 496 0.613 0.493 ±2.8%

Summer Seasonal Peak Hours (90% of 50/50 Peak)

Table i - 21: Summer Seasonal Peak Coincidence Factors Large and Small C&I Lighting

Sample Size Calculated Calculated Calculated Program Type n CF CV Rel Precision Large C&I 408 0.595 0.590 ±3.7% Small C&I 496 0.431 0.738 ±4.2%

Winter Seasonal Peak Hours (90% of 50/50 Peak)

Table i - 22: Winter Seasonal Peak Coincidence Factors Large and Small C&I Lighting

Comparison of On-Peak and Seasonal Peak CFs for C&I Lighting

Table i - 23 provides a comparison of the Summer On-Peak and Seasonal Peak CF for the Large and Small C&I Lighting, which shows that the On-Peak CF is higher than the Seasonal Peak CF. This is due to inclusion of more evening hours in the Seasonal Peak CF calculation when the percent on for the lighting is lower. This means that if the C&I Lighting measures were classified as Summer Seasonal Peak assets instead of Summer On-Peak assets the demand reduction would be lower for both Large and Small C&I Lighting. Table i - 24 provides the same comparison for the Winter On-Peak and Seasonal Peak CFs for C&I Lighting. In this case the Winter Seasonal Peak CFs are higher than the Winter On-Peak CFs. This indicates that for the Winter, both Large and Small C&I Lighting would have more demand reduction if classified as a Seasonal Peak asset.

2007 Coincidence Factor Study________________________________________ Page XV

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% Change On-Peak Seasonal Seasonal/ Program Type CF CF On-Peak Large C&I 0.736 0.714 97% Small C&I 0.661 0.613 93%

Summer

Table i - 23: Comparison of Summer On-Peak and Seasonal Peak CFs C&I Lighting % Change On-Peak Seasonal Seasonal/ Program Type CF CF On-Peak Large C&I 0.576 0.595 103% Small C&I 0.418 0.431 103%

Winter

Table i - 24: Comparison of Winter On-Peak and Seasonal Peak CFs C&I Lighting

Commercial & Industrial Interactive Demand Coincidence Factors

Table i - 25 and Table i - 26 provides the Interactive Summer and Winter On-Peak CFs for the Large and Small C&I sectors. For the Summer, the Large C&I has the higher Interactive On-Peak CF of about 0.14, while the Small C&I had an Interactive Summer On-Peak CF of 0.13. The Coefficient of Variation (CV) and relative precision are also provided and the relative precisions for both the Large and Small C&I CFs are better than ± 6% at the 80% confidence interval. For the Winter, the Small C&I sector has the higher Interactive On-Peak CF of about -0.05, while the Large C&I sector had an Interactive Winter On-Peak CF of -0.004. The Coefficient of Variation (CV) and relative precision are also provided and the relative precisions for both the Large C&I CF was ± 72% and the Small C&I CF was ± 27% at the 80% confidence interval. This was because CFs are so small and the coefficients of variation are so large.

Sample Size Calculated Calculated Calculated Program Type n CF CV Rel Precision Large C&I 376 0.139 0.718 ±4.7% Small C&I 425 0.125 0.907 ±5.6%

Interactive Summer On-Peak Hours 1PM - 5PM

Table i - 25: Interactive Summer On-Peak Coincidence Factors C&I Lighting^2

(^2) The number of log files (n) is lower than in the previous tables because we were unable to determine the maximum lighting demand reduction and were therefore unable to calculate the interactive demand reduction. In order to calculate the interactive CF it was necessary to calculate the interactive demand