Power load forecasting based on long short term memory-singular spectrum analysis

Abstract

Electrical load forecasting, mainly short-term load forecasting (STLF), plays a vital role in efficient power system planning by making it more intelligent, sustainable, and reliable. However, due to the presence of skewness and irregularities in the observed data, it becomes a challenging task to improve the accuracy of STLF. To handle this, we propose a new model, named Singular Spectrum Analysis-Long Short Term Memory (SSA-LSTM). SSA is used to remove the high-frequency components (high volatility data) from the load series. Based on the resultant series obtained from SSA, LSTM model forecasts the final load. We have used six publicly available datasets to evaluate the performance of our proposed model. With Kaggle Global Energy Forecasting Competition (GEFCom2012) dataset, the generated analytical results show 59.83% improvement over Tao’s benchmark model and 12.46%, 12.48%, 4.17%, 5.42%, 5.68%, 3.28%, 4.73% improvement over refined multi-linear regression, Moving average and Exponential smoothing (MA & ES), Support Vector Regression (SVR) model, feed-forward neural network (FNN), deep belief network (DBN), LSTM and Singular Spectrum Analysis (SSA) respectively. Our model has outperformed MA & ES, SVR, FNN, DBN, ensemble deep learning method (EDBN), SSA and LSTM in South Australia, Tasmania, Queensland, and Victoria electrical load dataset by 11.38%, 23.54%, 3.05%, and 1.39% respectively using RMSE error metric.