PROJECT TITLE :
Nonparametric Demand Forecasting and Detection of Energy Aware Consumers
To extend the reliability of the facility grid and cut back the chance of power provide failure, demand-facet management (DSM) is of central importance. During this paper, a nonparametric take a look at is applied to detect if the demand behavior of consumers is per time-of-day electricity tariff initiatives. The test is based on Afriat’s theorem in economics and has the unique feature that it provides necessary and sufficient conditions to detect if the price-demand behavior is consistent with utility maximization (i.e., the test detects demand-responsive consumers) without prior knowledge of the buyer’s utility perform. For customers that are tuned in to time-of-day pricing initiatives, a nonparametric learning algorithm is used to forecast power demands for unobserved electricity tariffs. The nonparametric learning algorithm will be utilized in anticipatory management structures in a DSM framework to attain power usage objectives. Real-world knowledge from Ontario’s power system and numerical examples illustrate the accuracy of the nonparametric test and nonparametric learning algorithm for forecasting consumer demand.
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