Harnessing Machine Learning and Meta-Heuristic Algorithms for Accurate Cooling Load Prediction

Precisely calculating the cooling load is essential to improving the energy efficiency of cooling systems, as well as maximizing the performance of chillers and air conditioning controls. Machine learning (ML) has better capabilities in this area than conventional techniques and regression analysis, which are lacking. ML models are capable of automatically recognizing complex patterns that are influenced by various factors, including occupancy, building materials, and weather. They enable responsive predictions that enhance energy optimization and efficient building management because they scale well with data and adapt to changing scenarios. This research acknowledges the difficulties presented by the intricacies of energy optimization while exploring the intricate world of cooling load systems. To solve these issues, in-depth research and creative approaches to problem-solving are needed. The Weevil Damage Optimization Algorithm (WDOA) and the Improved Manta-Ray Foraging Optimizer (IMRFO) are two meta-heuristic algorithms that are seamlessly combined with the Gaussian Process Regression (GPR) model in this study to increase accuracy. Previous stability tests have provided extensive validation for the cooling load data used in these algorithms. The research presents three different models, each of which offers important insights for precise cooling load prediction: GPWD, GPIM, and an independent GPR model. With an RMSE value of 1.004 and an impressive R-2 value of 0.990, the GPWD model stands out as the best performer among these models. The remarkable outcomes demonstrate the outstanding precision of the GPWD model in forecasting the cooling load, highlighting its applicability to actual building management situations.