An Innovative Machine Learning Technique for the Prediction of Weather Based Smart Home Energy Consumption

Abstract

The prediction of energy consumption plays a significant role in energy conservation and reducing the cost of power generation, to improve energy sustainability and economic stability. Current studies show an increased interest in the application of Machine Learning algorithms to forecast energy utilisation in smart homes. The performance of these Machine Learning algorithms is evaluated using accuracy algorithms. The process of manually selecting best-performing Machine Learning algorithms is still very challenging for data analysts and decision makers because the algorithms might not work well in a different use case or data-set. To address this, we propose a decision algorithm model using machine learning based data mining and picture fuzzy operators. First, Machine Learning algorithms are trained and tested to predict energy consumption of smart home appliances with respect to the weather information. Second, score values of Lasso Regression are used to understand the patterns and features of weather information for smart house micro-climate. We then propose a decision matrix using fuzzy operators to aggregate Machine Learning algorithms, prior to ranking using a score function. Finally, the electricity consumption of appliances as well as total energy consumed in the smart home is provided in Kilowatts (KW).