P-ConvLSTM: An Effective Parallel ConvLSTM-based model for Short-term Electricity Load Forecasting

Abstract

Abstract Short-term Load Forecasting (STLF) is a challenging task for an Energy Management System (EMS) that depends on highly unpredictable and volatile factors, making it difficult to predict the electricity load demand accurately. Despite the challenges, it is an essential component, as it helps to ensure energy demand-supply equilibrium, prevents blackouts, reduces the need for expensive peak power generation, and improves the efficiency and reliability of the EMS. Motivated by these factors, we have proposed a novel STLF framework using a multi-input parallel ConvLSTM model. The effectiveness of the proposed model is verified using two publicly available load-series datasets. On the Malaysia dataset, the proposed model yields 998.12, 2.59%, 1590.34, and 0.987 for MAE, MAPE, RMSE, and R2, respectively. Similarly, on the Tetouan dataset, this model yields 1737.32, 2.49%, 2254.91, and 0.976 for MAE, MAPE, RMSE, and R2, respectively. These outperforming results found in the comparative experiments are further statistically verified using Friedman's test. The presenting framework of STLF can help EMS to make informed decisions about resource allocation and system operations.