Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system

Abstract

Electricity is an essential resource for human production and survival. Accurately predicting electrical load consumption can help power supply companies make informed decisions, such as peak load shifting, to maintain a reliable power supply and reduce CO2 emissions. However, forecasting electricity consumption is challenging due to the nonlinear and nonstationary time series data that is correlated with climate change. To address this challenge, this paper proposes an electricity forecasting method based on empirical mode decomposition (EMD) and bidirectional LSTM. EMD is a solid and robust instrument for time–frequency analysis and signal preprocessing, which separates the time series into components at different resolutions. The proposed model predicts the future 24 h with a resolution of 15 min by creating many stationary component sequences from the original stochastic electricity usage time series data (IMFs). To predict each Intrinsic Mode Function, a hybrid model BI-LSTM is employed. The results of each component's forecast are then merged to give the overall forecast. Two comparative studies are conducted to justify the choice of the signal processing method and the prediction algorithm. The proposed model demonstrates a minimal MAPE of 0.28% and a better R2 close to 1 of 0.84 compared to other papers.