Predicting the energy consumption in buildings using the optimized support vector regression model

One of the most significant axes of regional, national, and worldwide energy policy is energy efficiency in building design. In particular, the energy efficiency of HVAC systems is of paramount importance. They provide energy for both residential and commercial sectors. As a result, assessing building energy usage is a critical step in optimizing building energy consumption. The impact of eight input factors on the two output variables, heating and cooling loads, for residential structures was explored in this study. For this purpose, the SVR-supervised machine learning algorithm was used. Despite its advantages, such as predictive accuracy and robustness, this method suffers from the fact that there is no specific rule for fitting its parameters. Therefore, six meta-heuristic optimization algorithms were investigated, and the strongest algorithm for optimal parameter fitting for the SVR model was presented. The correlation and error parameters analysis showed that the hybrid model SVR-AEO has the best performance in simulating residential buildings' heating and cooling loads. Ac-cording to the obtained results, the value of R2 for the cooling and heating loads prediction in the training data is obtained to be 0.9975 and 0.99955, respectively.