Short-term electrical load forecasting through heuristic configuration of regularized deep neural network

Abstract

An accurate electrical load forecasting is essential for optimal grid operation. The paper presents a methodology for the short-term commercial building electrical load forecasting through a regularized deep neural network: Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). Detailed heuristic analysis regarding relevant input feature selection, the volume of training data, hyperparameter tuning and regularizer selection of an optimal LSTM-RNN network configuration is presented. The regularized LSTM-RNN is used to forecast 30-min and 24-h ahead electrical loads of two commercial buildings in Virginia, USA. The forecast is performed for one week each over four different months in 2019: January, April, July and October to represent four different seasons in North America. The performance of electrical load forecasts has been compared against actual smart meter data from the electric utility of these buildings. For the case study presented, Mean Absolute Percentage Error (MAPE%) with the regularized LSTM-RNN is 4.9%, compared to 6.4%, 9.2% and 13.3% with Shallow-ANN (Artificial Neural Network), Support Vector Regression (SVR) and Linear Regression (LR) respectively for 30-min ahead electrical load forecast. For 24-h ahead electrical load forecast, MAPE (%) is 11.6%, compared to 12.7%, 13.4% and 14.3% with shallow-ANN, SVR and LR respectively. The methodology to configure a deep neural network (LSTM-RNN) for electrical load forecasting presented in this paper can be utilized for optimal forecasting performance.