Investigating Cooling Load Estimation via Hybrid Models Based on the Radial Basis Function

Abstract

To advance energy conservation in cooling systems within buildings, a pivotal technology known as cooling load prediction is essential. Traditional industry computational models typically employ forward or inverse modeling techniques, but these methods often demand extensive computational resources and involve lengthy procedures. However, artificial intelligence (AI) surpasses these approaches, with its models exhibiting the capability to autonomously discern intricate patterns, adapt dynamically, and enhance their performance as data volumes increase. AI models excel in forecasting cooling loads, accounting for various factors like weather conditions, building materials, and occupancy. This results in agile and responsive predictions, ultimately leading to heightened energy efficiency. The dataset of this study, which comprised 768 samples, was derived from previous studies. The primary objective of this study is to introduce a novel framework for the prediction of Cooling Load via integrating the Radial Basis Function (RBF) with 2 innovative optimization algorithms, specifically the Dynamic Arithmetic Optimization Algorithm (DAO) and the Golden Eagle Optimization Algorithm (GEO). The predictive outcomes indicate that the RBDA prediction model outperforms RBF in cooling load predictions, with RMSE=0.792, approximately half as much as those of RBF. Furthermore, the RBDA model's performance, especially in the training phase, confirmed the optimal value of R 2 =0.993.