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cooling load. From 11 a.m. through 10 p.m. (hours 11 through 22), the right side of the distribution increases markedly, showing the air-conditioning load with increased clarity. As a final step, models can be fit to each of the disaggregated end-use subtotals. Figure 3.5 shows non-linear, non-parametric fits (a lowess curve) for each of three end-use load subtotals for hour 15 (3 p.m.). These are stacked, i.e., the top curve represents the total load. The end-use load is the distance between the curves (or the x-axis). Figure3.5. Non-LinearModelsofThreeEnd-UseLoadSubtotalsforHour15(3p.m.) Such models provide several additional types of information. First, the subtotals themselves form a much finer end-use resolution with which to spot changes in consumption for smaller appliances and loads from lights and electronics. Second, it should be noted that the consumption for water heating and for lighting plus “other” loads (the rest of the appliances in the home) are not constant throughout the year, but instead have a pronounced linear trend increasing with colder outside temperatures and winter months. This is typical of U.S. residences that have been metered at the end-use level, in which virtually all non-HVAC end uses increase 10% to 20% or more from summer to winter, on average (Pratt et al. 1993). Thus, the assumption of PRISM-like methods−that the base load is a constant−tends to overstate heating and understate cooling loads. 3.15PDF Image | The Smart Grid: An Estimation of the Energy and CO2 Benefits
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