Mining Customers' Changeable Electricity Consumption for Effective Load Forecasting

Most existing approaches for electricity load forecasting perform the task based on overall electricity consumption. However, using such a global methodology can affect load forecasting accuracy, as it does not consider the possibility that customers' consumption behavior may change at any time. Predicting customers' electricity consumption in the presence of unstable behaviors poses challenges to existing models. In this article, we propose a principled approach capable of handling customers' changeable electricity consumption. We devise a network-based method that first builds and tracks clusters of customer consumption patterns over time. Then, on the evolving clusters, we develop a framework that exploits long short-term memory recurrent neural network and survival analysis techniques to forecast electricity consumption. Our experiments on real electricity consumption datasets illustrate the suitability of the proposed approach.