Robust and Accurate Short-Term Load Forecasting: A Cluster Oriented Ensemble Learning Approach

Abstract

One of the most critical tasks for operating a power system is load forecasting in order to keep balance between demand and supply and for planning infrastructure. Errors in load forecasting can result in significant cost increases for electricity suppliers and increase the chance of unexpected blackouts or brownouts. Improving the accuracy of short term load forecasting is a challenging open problem. This paper proposes a novel framework for short-term load forecasting using an effective new combination of c-Shape clustering, LSTM networks and Xgboost methods. In particular, our proposed approach introduces an ensemble process together with novel features that lead to improved accuracy of the load forecasting model. The performance of the proposed framework is validated with publicly available real-life data from Australian Energy Market Operator, as well as through on-site deployment, which has led to substantially higher accuracy over existing methods.