Long short-term memory-singular spectrum analysis-based model for electric load forecasting

Abstract

Electrical load forecasting is a key player in building sustainable power systems and helps in efficient system planning. However, the irregular and noisy behavior in the observed data makes it difficult to achieve better forecasting accuracy. To handle this, we propose a new model, named singular spectrum analysis-long short- term memory (SSA-LSTM). SSA is a signal processing technique used to eliminate the noisy components of a skewed load series. LSTM model uses the outcome of SSA to forecast the final load. We have used five publicly available datasets from the Australian Energy Market Operator (AEMO) repository to assess the performance of the proposed model. The proposed model has superior forecasting accuracy compared to other existing state-of-the-art methods [persistence, autoregressive (AR), AR-exogenous, ARMA-exogenous (ARMAX), support vector regression (SVR), random forest (RF), artificial neural network (ANN), deep belief network (DBN), empirical mode decomposition (EMD-SVR), EMD-ANN, ensemble DBN, and dynamic mode decomposition (DMD)] for half-hourly and one day ahead load forecasting using RMSE and MAPE error metrics.