Short-Term Load Forecasting with LSTM Based Ensemble Learning

In this paper, a short-term load forecasting framework with long short-term memory (LSTM)-based ensemble learning is proposed. To fully exploit the correlation in data for accurate load forecasting, the data is first clustered and each cluster is used to train an LSTM model. Then a Fully Connected Cascade (FCC) Neural Network is incorporated for ensemble learning, which is solved by an enhanced Levenberg-Marquardt (LM) training algorithm. The proposed framework is tested with a public dataset, where its superior performance over several baseline schemes is demonstrated.