Evaluation and Comparison of Energy Consumption Prediction Models Case Study: Smart Home

Abstract

Predicting a structure’s energy usage is an essential part of achieving energy efficiency objectives. Engineering, AI-based, and hybrid approaches can all be used to predict how much energy a building will require; however, we choose the AI-based method because it uses historical data to make predictions about future energy usage rather than thermodynamic equations the other approaches rely on. As a result, the objective of this study is to put several prediction models for energy usage into practice and assess them, the recommended algorithms are linear regression, random forest, and artificial neural network. Our study’s data set was gathered from a house that served as a case study, and we compared each approach’s efficacy using RMSE, R squared, MAE, and MAPE measurements.