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Option C- Utility Bill Comparison Methodology Option C may be applied to projects in which (a ) the potential to generate savings must be verified and ( b ) actual energy use during the contract term must be measured for comparison with the baseline model for calculating savings . Option C involves procedures for verifying the same items as Option A plus determining energy savings during the contract term through the use of whole-building metering data . All end -use technologies can be verified with Option C , provided that the reduction in consumption is larger than the associated modeling error and that the ECMs are inter related . This option may be used in cases in which there is a high degree of interaction between installed energy conservation systems and/or the measurement of individual component savings is not cost-effective . Accounting for changes ( other than those caused by the ECMs) is the major challenge associated with Option C , particularly for long -term contracts . The following points should be considered when conducting Option C analyses for M &V : 1 . All explanatory variables that affect energy consumption as well as possible interactive terms ( i . e . , combination of variables) must be specified , whether or not they are accounted for in the model . Critical variables can include weather, occupancy patterns , set points , and operating schedules . 2. Independent variable data will need to correspond to the time periods of the billing meter reading dates and intervals . 3 . If the energy savings model incorporates weather data , the following issues should be considered : Use of the building "temperature balance point" for defining degree-days versus an arbitrary temperature base . • The relationship between temperature and energy use that tends to vary depending upon the time of year. For example , an ambient temperature of 550F in January has a different implication for energy usage than the same temperature in August. Thus , seasons should be addressed in the model . • The nonlinear response to weather. For example , a 10° F change in temperature results in a very different energy use impact if that change is from 75° F to 85 ° F rather than 35° F to 45° F . • Matching degree -day data with billing start and end dates . 4 . The criteria used for identifying and eliminating outliers must be documented . Outliers are data beyond the expected range of values ( or two to three standard deviations away from the average of the data ) . Outliers should be defined using common sense as well as common statistical practice . 5 . Statistical validity of the final regression model must be demonstrated . Validation steps include checks to make sure that: • The model makes intuitive sense ; that is , the explanatory variables are reasonable and the coefficients have the expected sign ( positive or negative) and are within an expected range ( magnitude ) . Modeled data are representative of the population . Model form conforms to standard statistical practice . The number of coefficients is appropriate for the number of observations ( approximately no more than one explanatory variable for every five data observations ) . All model data are thoroughly documented , and model limits ( range of independent variables for which the model is valid ) are specified . Option C usually requires at least 9 to 12 months of continuous data before a retrofit and continuous data after the retrofit; the data can be hourly or monthly whole-building data . Indian River County , Florida 21 Indian River County Phase 2 Phase 11 Rev . 10/09