Long short term memory networks for short-term electric load forecasting

Abstract

Short-term electricity demand forecasting is critical to utility companies. It plays a key role in the operation of power industry. It becomes all the more important and critical with increasing penetration of renewable energy sources. Short-term load forecasting enables power companies to make informed business decisions in real-time. Demand patterns are extremely complex due to market deregulation and other environmental factors. Although there has been extensive research in the area of short-term electrical load forecasting, difficulties in implementation and lack of transparency in results has been cited as a main challenge. Deep neural architectures have recently shown their ability to mine complex underlying patterns in various domains. In our work, we present a deep recurrent neural architecture to unearth the complex patterns underlying the regional demand profiles without specific insights from the utilities. The model learns from historical data patterns. We show that deep recurrent neural network with long-short term memory architecture presents a robust methodology for accurate short term load forecasting with the ability to adapt and learn the underlying complex features over time. In most cases it matches the performance of the latest state-of-the-art techniques and even supercedes it in a few cases.