Analytical study of machine learning techniques on the smart home energy consumption.

In the present time, the demand of energy is increasing rapidly. The heavy demand of energy is in the smart cities domain as there are number of sub domains like Smart Homes, Smart Transportation, Smart Healthcare, etc. So, as the result the domain is opening the new research direction for the industrialist, researchers, and scientists. They are attracting towards the domain of smart city for different kinds of research projects. The primary concern of this paper is to focus on the energy consumption of smart homes. In this paper different Machine Learning (ML) and Deep Learning (DL) models are being implemented and the results are analyzed to check the performance of the different models on the dataset of smart home energy consumption. This study found that the Random Forest is predicting the energy consumption with lowest error with 0.6616, 0.4377, 0.3171 RMSE, MSE, and MAE respectively. The performance of the models is being evaluated in the terms of Root Mean Square Error (RMSE), Mean Square Error (MSE), and Mean Absolute Error (MAE). This study found that the most suited model for energy consumption data in smart city is Random Forest. [ABSTRACT FROM AUTHOR]