Deep sequence to sequence Bi-LSTM neural networks for day-ahead peak load forecasting

Abstract

The power industry is currently facing the problem of an electricity supply–demand imbalance. The most inexpensive and efficient solution to alleviate this imbalance is to decrease electricity demand. Local electrical utilities should deploy demand response programs (DRP), and short-term peak demand forecasting (STPDF) plays a crucial role in their successful deployment. In residential sectors, peak demand forecasting is also critical because the energy policies, technological growth, and changing climate are further increasing the peak demand. Therefore, an accurate peak demand forecasting will help utility companies in avoiding blackouts and secure a continuous power supply by implementing subsidy schemes such as DRP. However, daily peak load is volatile, nonstationary, and nonlinear in nature, and hence it is hard to predict it accurately. This research work for the first time has attempted to design, implement, and test deep bidirectional long short-term memory based sequence to sequence (Bi-LSTM S2S) regression approach for “day-ahead” peak demand forecasting and has accomplished preliminary success. The day-ahead peak electricity demand forecasting model is designed and tested using the MATLAB software. For performance comparison, shallow Bi-LSTM S2S, shallow LSTM S2S, deep LSTM S2S, Levenberg-Marquardt backpropagation artificial neural networks (LMBP-ANN), and medium Gaussian support vector regression (MG-SVR) forecasting models are also developed and tested. Mean absolute percentage error (MAPE) and Root Mean Squared Error (RMSE) are used as performance metrics. It has been found out that in terms of both performance metrics, the proposed deep Bi-LSTM S2S day-ahead “peak” forecasting model has outperformed all the other models on both public holidays and normal days. The load pattern on public holidays is always different than on normal days, and there is always less data available in contrast to the normal days. Therefore, it is hard to accurately forecast their load.