A machine-learning ensemble model for predicting energy consumption in smart homes

TLDR: In this paper , the authors performed an overall analysis of energy consumption in smart homes by deploying machine learning models, such as Decision Trees (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and k-Nearest Neighbor (KNN) for predicting the power consumption of multiple datasets.

Abstract: Smart homes incorporate several devices that automate tasks and make our lives easy. These devices can be useful for many things, like security access, lighting, temperature, etc. Using the Internet of Things (IoT) platform, smart homes essentially let homeowners control appliances and devices remotely. Due to their self-learning skills, smart homes can learn homeowners' schedules and adapt accordingly to make adjustments. Since convenience and cost savings is necessary in such an environment, and there are multiple devices involved, there is a need to analyze power consumption in smart homes. Moreover, increased energy consumption leads to an increase in carbon footprint, elevates the risk of climate, and leads to increased demand in supply. Hence, monitoring energy consumption is crucial. In this paper, we perform an overall analysis of energy consumption in smart homes by deploying machine learning models. We rely on machine learning techniques, like Decision Trees (DT), Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and k-Nearest Neighbor (KNN) for predicting the power consumption of multiple datasets. We also propose a DT-RF-XGBoost-based Ensemble Model for analyzing the consumption and comparing it with the baseline algorithms. The evaluation parameters used in the study are Mean Square Error (MSE), R-squared (R 2, ), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE), respectively. The study has been performed on multiple datasets and our study shows that the proposed DT-RF-XG-based Ensemble Model outperforms all the other baseline algorithms for multiple datasets with R 2 around 0.99.