Investigating Enhanced Cooling Load Estimation through Hybrid LSSVR Models - Ali Hassan; Hamza Rashid (Journal of Artificial Intelligence and System Modelling)
Rising global urbanization necessitates accurate energy consumption prediction, particularly for residential buildings. Given the significant influence of cooling systems on operational costs, it is valuable to explore models for forecasting building heating and cooling loads. Unlike many prior studies relying on historical energy data, this research emphasizes incorporating building attributes, including area and floor height, in developing predictive models for cooling loads. Cooling load prediction technology is pivotal to boosting energy efficiency in cooling systems. Conventional computational models, encompassing both forward and inverse modeling, demand substantial computational resources and prolonged procedures. Conversely, artificial intelligence exhibits superior performance, employing adaptable models adept at pattern recognition and self-improvement as data accumulates. These models excel at forecasting cooling loads, incorporating weather conditions, building materials, and occupancy, yielding swift, responsive predictions that augment energy efficiency. The utilized dataset contains 768 samples, which are extracted from published articles. This research examines the application of Least Squares Support Vector Regression (LS-SVR) in predicting cooling load consumption for buildings. The LS-SVR model is enhanced through optimization techniques such as Sea Horse Optimizer (SHO) and Runge Kutta Optimization (RKO). The results reveal that the LSSVR+SHO (LSSH) prediction model surpasses the LSSVR in cooling load predictions, as reflected in its Root Mean Square Error (RMSE) of 1.019, which is approximately 0.666 lower than the LSSVR. Additionally, the LSSH model exhibits exceptional performance, particularly during the training phase, achieving an optimal R2 value of 0.988.