Forecasting energy demand and efficiency in a smart home environment through advanced ensemble model: Stacking and voting

Abstract

Smart homes integrate several sensors to facilitate information exchange and the execution of tasks. In addition, with the development of the Internet of Things (IoT) platforms, the control of appliances and remote devices has become possible. This sensor collects data in real time to closely monitor the devices of a user’s household. The present study employs a machine learning methodology to perform a global analysis of energy consumption and efficiency in smart homes. In This work we propose two advanced ensemble models to improve the performance of energy consumption in smart homes, the first one is a voting ensemble model based on a ranking weight averaging that combines following basic machine learning techniques: decision tree (DT), random forest (RF), and eXtreme Gradient Boosting (XGB). The second one is the stacking ensemble model in which the basic models (DT-RF-XGB) are combined through stacked generalization, then uses a secondary layer model or meta-learner (RF) to provide output prediction. The findings obtained show that the proposed ensemble model based on DT-RF-XGB using stacking technique surpasses all other basic algorithms with R2 around 0.9825.