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(Mechanism D). It can also be used to “mine” for customer-specific energy efficiency and demand response opportunities that have been overlooked, and as the basis for enhancing feedback to consumers on their individual appliances and end uses, as discussed in Section 3.1.1 (Mechanism A). Thus, the addition of relatively straightforward automated analytic processes coupled with smart grid measurement and communication capabilities forms a key enabler for obtaining energy and carbon benefits. The following four figures help show the improved end-use resolution that could be provided by smart grid technology. Figure 3.2 shows the monthly energy consumption profile for a residence from data obtained by a standard utility meter. The home is located in a hot desert climate with high summer peak load demand due to dozens of days in which the outside temperature exceeds 100oF. With a monthly resolution, the energy consumption is actually at its lowest in the summer, making it impossible to discern much about the end-use consumption of the home without the use of an engineering model and a lot of assumptions that introduce considerable uncertainty. Figure3.2. MonthlyEnergyBillsfromTypicalHomeinaHotDesertClimate Figure 3.3 shows a profile of the same monthly energy bills plotted against the average monthly temperature. This is the technical basis for a well-known standard technique for analyzing monthly billing data, originally called the Princeton Scorekeeping Method (PRISM; Fels and Reynolds 1993). The PRISM method attempts to find the best statistical fit for three lines describing the heating, cooling, and base load (all other loads) for a building. The base load is estimated from the minimum monthly bills, assuming that it is relatively constant. Thus, the estimate of the base load is a horizontal line, as shown in Figure 3.3. When the home is heated by electricity, the monthly load is expected to increase as the outdoor temperature decreases. The physics of heat flow suggests this relationship is linear with temperature, although when aggregated to the monthly level, there tends to be somewhat of an upward bend to this curve. The deviations in the monthly winter consumption data from the linear assumption in Figure 3.3 are illustrative of this effect. If the heating is provided by a heat pump, this curvature is greatly 3.12PDF Image | The Smart Grid: An Estimation of the Energy and CO2 Benefits
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