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hour of the day provides much additional clarity. Third, with the additional use of the on/off signal that could be provided by a smart thermostat, the total consumption provided by a smart meter can be disaggregated into subtotals for heating, cooling, and “other” base loads with reasonable precision. Figure3.4. HourlyLoadDatavs.Temperature,OnePlotforEachHour This process is entirely analogous to what has been termed non-intrusive load monitoring (NILM; Drenker and Kader 1999). NILM uses short interval readings from a meter and uses a cluster analysis to look for common changes in the level of power consumption. These can then be mapped to a specific major appliance, e.g., a space heater, air conditioner, or water heater, either by 1) rules of thumb, if the consumption level is relatively standard, as for water heaters, or 2) manually activating major appliances and denoting the cluster into which the resulting load is mapped. The same basic process can be applied to a smart-grid-based system by using the on/off status signal from the thermostat instead of the cluster analysis. This provides an unequivocal signal with which to flag on/off events. More importantly, for mass deployments in which the process must be completely automated, it inherently associates the changed consumption level to a specific appliance or load, eliminating the need for the manual tests. Integrating the resulting on/off events and power levels over time produces a good breakdown of the end-use composition of the total load collected by a smart meter. Figure 3.4 provides separate plots of the HVAC energy consumption for each hour of the day as a function of the outside temperature for that hour. The air-conditioning load becomes readily apparent with this more granular data. In hours 1 a.m. through 10 a.m. (hours 1 through 10), there is no apparent 3.14PDF Image | The Smart Grid: An Estimation of the Energy and CO2 Benefits
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