Stacking Machine Learning Models to Forecast Hourly and Daily Electricity Consumption of Household Using Internet of Things

Abstract

The objective of this paper is to design an efficient electricity consumption forecasting model using stacking ensemble technique and Internet of Things (IoT). Two stage process is applied in this paper. In the first stage, fifteen forecasting models (Auto-ARIMA, Holt-Winter (Additive), Exponential, Facebook Prophet, Light Gradient Boosting, AdaBoost, Support Vector Regression, Decision Tree, Extra Tree, Random Forest, Elastic net, K-Nearest Neighbour’s, XGBoost, Linear Regression, Long Short Term Memory) are applied to forecast electricity consumption at an hourly and daily level. In the next stage, the best four models are selected and stacked. We have considered the dataset of energy consumption by electrical appliances per minute in a house over seven days. The models are evaluated using root mean square error (RMSE), mean absolute error (MAE), R-square, and mean absolute percentage error (MAPE). The results show that the extra tree performed better among all the algorithms, and stacking further improves performance. Elastic net and decision tree algorithms have taken less time as compared to other models applied in this study.