Short-term Load Forecasting on Smart Meter via Deep Learning

Abstract

Smart metering has grabbed significant attention in recent years, particularly for the utility providers who plan the energy resources and take control actions to balance the electricity demand and supply by load forecasting. Currently, load forecasting is performed at the aggregated level, not at an individual level because it is highly uncertain and complex. Specifically, the performance of short-term forecasting is affected significantly by the variance of load uncertainty. Moreover, limited work has been done to help users choose the optimal usage plan. In this paper, we evaluate several deep learning models for load forecasting. In addition, we employ deep learning techniques to provide the optimal power plan for users based on their power usage. Experimental results using the data from the Irish Social Science Data Archive demonstrate the effectiveness of the proposed schemes.