Comparative Analysis of Regression Learning Methods for Estimation of Energy Performance of Residential Structures

Abstract

Energy efficiency is a top priority for private and commercial buildings. This study evaluates the performance of six regression learning methods, including Linear Regressor, MLP Regressor, RBF Regressor, SVM Regressor, Gaussian Processes, and ANFIS Regressor to predict the heating and cooling loads of residential buildings. 768 buildings were considered and analyzed based on the influential parameters, such as relative density, surface area, wall area, roof area, overall height, orientation, glazing area, and glazing area distribution for predicting heating load and cooling load. Three statistical criteria such as correlation coefficient (R), mean absolute error (MAE) and root mean square error (RMSE) were used to assess the potential of the regression methods used in this study. The best estimation results were obtained with the ANFIS regression model, with

Machine Learning Models for the Prediction of Energy Consumption Based on Cooling and Heating Loads in Internet-of-Things-Based Smart Buildings

“…Because there was no priority among the original dataset rows and having the same underlying distribution, this work used the common rule of 70% for training data and 30% for testing data in the preprocessing phase of splitting in the first and third experiments. This ratio was preferred, with the aim of providing comparability since some previous studies [25,36,39,40,42] used it. Moreover, k-fold cross-validation was used in the second and fourth experiments for the evaluation of the performances of the models.…”