A Stacking Multi-Learning Ensemble Model for Predicting Near Real Time Energy Consumption Demand of Residential Buildings

The aim of this paper is to present a novel energy consumption forecasting solution for predicting energy demand at the level of residential buildings based on their historical consumption profile for a seamless integration with the session-based Energy Markets developed within Smart Energy Grids that integrate renewable energy sources. To overcome the drawbacks and lack of accuracy of existing prediction models, a stacked multi-learning ensemble model is proposed combining Gradient Boosting Regression, Multi-Layer Neural Networks and Long Short Term Memory Networks followed by a Linear Regressor for forecasting residential energy demands both at individual and aggregated levels. The proposed ensemble predictor is evaluated using the open-access UK-DALE dataset containing historical energy traces for 5 households spreading over several years, obtaining a best MAPE of 1.59%, a RMSE of 6. 19 kWh and a MAE of 5. 60 kWh on the aggregated dataset, proving the high accuracy and stability of the proposed solution as well as the feasibility of using ensemble models for residential building energy demand forecast for integration with session-based energy marketplaces.