A Comparative Analysis of Extreme Gradient Boosting Technique with Long Short-Term Memory and Layered Recurrent Neural Network for Electricity Demand Forecas

Abstract

In the energy sector, for an efficient electricity load management which includes viable utilization and allocation of energy assets, Electricity Load Forecasting plays a critical role. Precise long-term and short-term electricity demand forecast is significant as it enables complete utilization of produced electric power, preventing over-production and sometimes wastage of energy and resources. This paper presents a comparative proof of ensemble learning based algorithm Extreme Gradient Boosting Technique (XGBoost) with Deep Recurrent Neural Network (RNN) and Stacked Long Short-Term Memory Network (LSTM) for short term electricity demand forecast on the Dominion Energy Data taken from PJM energy market. The aim of this paper is to prove that stacked LSTM performs better as compared to an ensemble machine learning model XGBoost and deep RNN algorithms on PJM energy data, by using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and R2 score as evaluation metrics for performance validation. This work sheds light on the internal architecture of the models and the different values of hyper-parameters used while training the models to justify the observed day-ahead predictions.