Ensemble learning models for short-term electricity demand forecasting

Abstract

Forecasting energy consumption is critical in decision-making for efficient energy saving, improve stability of the power grid, and prevent supply-demand discrepancy. To predict day-ahead load forecasting for the demand of city of Kirkuk two scenarios were presented. First, benchmarked three individual machine learning algorithms e.g. generalized linear model (GLM), artificial neural network (ANN), and random forest (RF). Second, compared the predictive capabilities for individual models with the ensemble models. The results indicate that the predictive models maybe can be improved using simple ensemble learning strategies such as averaging the predicted results. This study is also present future research directions to improve the model prediction capabilities.