Prediction of room temperature in Trombe solar wall systems using machine learning algorithms

Abstract

A Trombe wall-heating system is used to absorb solar energy to heat buildings. Different parameters affect the system performance for optimal heating. This study evaluated the performance of four machine learning algorithms—linear regression, k-nearest neighbors, random forest, and decision tree—for predicting the room temperature in a Trombe wall system. The accuracy of the algorithms was assessed using R² and RMSE values. The results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance, with R² and RMSE values of 1 and 0. In contrast, linear regression and decision tree showed weaker performance. These findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems, enabling informed design decisions to enhance energy efficiency.