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exaggerated, because it is much less efficient at supplying heat when it is colder. The scarcity of the monthly data and the effect of the curvature make it difficult to fit these lines with a high degree of confidence. Figure3.3. MonthlyEnergyBillsfromTypicalHomevs.MonthlyAverageTemperature Trying to estimate the air-conditioning loads in this home using monthly data is impossible. The relationship between consumption and temperature in the summertime is non-existent and estimating the end-use consumption is highly uncertain, particularly the expected air-conditioning load. It is not unreasonable to make the interpretation that there is no air-conditioning load in this home—in fact, this is far from the case. PRISM-type methods were developed for the purpose of energy efficiency program evaluations involving hundreds or thousands of buildings, rather than providing building-specific analyses. Despite their limitations, they are suitable for this purpose as long as any errors introduced by the limitations of the method are random from one building to the next. They are considerably less well suited to detailed analysis of individual buildings, as illustrated here. Figure 3.4 shows the increased information content that can be provided by leveraging smart grid assets. First, thousands of hourly intervals of consumption from a smart meter provide a much stronger statistical basis for fitting a line or a curve to the data. Second, examining the results separately for each 3.13PDF Image | The Smart Grid: An Estimation of the Energy and CO2 Benefits
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