Nonparametric Demand Forecasting and Detection of Energy Aware Consumers

Abstract

To increase the reliability of the power grid and reduce the risk of power supply failure, demand-side management (DSM) is of central importance. In this paper, a nonparametric test is applied to detect if the demand behavior of consumers is consistent with 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 consumer's utility function. For consumers that are responsive to time-of-day pricing initiatives, a nonparametric learning algorithm is used to forecast power demands for unobserved electricity tariffs. The nonparametric learning algorithm can be used in anticipatory control structures in a DSM framework to achieve power usage objectives. Real-world data from Ontario's power system and numerical examples illustrate the accuracy of the nonparametric test and nonparametric learning algorithm for forecasting consumer demand.