Distribution Long-term Load Forecast Using Long Short-Term Memory Network

Abstract

Long Short-Term Memory (LSTM) network is an enhanced Recurrent Neural Network (RNN) that has gained significant attention in recent years. It solved the vanishing and exploding gradient problems standard RNN has and has been successfully applied to a variety of time-series forecasting problems. In power systems, distribution feeder long-term load forecast is a critical task many electric utility companies perform on an annual basis. The goal of this task is to forecast the load growth on existing distribution feeders for the next one to ten years. The forecasted results will be used as input in long-term feeder planning studies to determine future distribution feeder configuration, feeder tie addition and transformer capacity addition so that the distribution system can operate reliably during normal operation and contingences. This research applied LSTM network to this important and challenging task. This paper firstly explained the concept and structure of LSTM and then discussed the details of data features and dataset preparation. The dataset is then fit into a LSTM network to establish the long-term load forecast model. In the end, an application example in a Canadian Utility company is provided. The results are compared and discussed. It is proven that the proposed approach is effective for distribution feeder long-term load forecast, especially for saturated feeders and high residential percentage feeders.