首页|Building heating energy demand estimated by random forest model in individual and hybrid approaches
Building heating energy demand estimated by random forest model in individual and hybrid approaches
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NETL
NSTL
Abstract Nowadays, because of the current world’s conditions, especially in terms of energy and economic security, environmental problems, and greenhouse gas in the climate change sector, decision-makers are forced to concentrate on sustainable development, particularly in the energy efficiency sector. Buildings are a critical source of energy consumption and greenhouse gas production, so estimating their energy usage is crucial in decreasing their impacts. This article intends to apply a Random Forest (RF) ensemble classifier, a frequently-used machine learning algorithm that reaches singular results for multiple decision trees for estimating building heating load. Artificial Rabbits Optimization (ARO) and Electric Charged Particles Optimization (ECPO) are used to boost accuracy and reduce total loss when estimating heating load. The study provides insight into building heating load predictions. It proposes the RFEC (Random Forest optimized with electrically charged particle optimization) model as the most efficient way to achieve optimized energy consumption with a maximum coefficient of determination of 0.994 and root mean square error of 0.776. According to the obtained values for R2 and RMSE, which are 0.974 and 1.481, respectively, for the simple RF model, when comparing them to the mentioned values, it is clear that by optimizing RF by ECPO, the R2 value has increased by approximately 1.5%, and the error rate has decreased by almost 91%.
NETL
NSTL
