Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction

The research tackles the complex problem of accurately predicting cooling loads in the context of energy efficiency and building management. It presents a novel approach that increases the precision of cooling load forecasts by utilizing machine learning (ML). The main objective is to incorporate a hybridization strategy into Radial Basis Function (RBF) models, a commonly used method for cooling load prediction, to improve their effectiveness. This new method significantly increases accuracy and reliability. The resulting hybrid models, which combine two powerful optimization techniques, outperform the state-of-the-art approaches and mark a major advancement in predictive modelling. The study performs in-depth analyses to compare standalone and hybrid model configurations, guaranteeing an unbiased and thorough performance evaluation. The deliberate choice of incorporating the Self-adaptive Bonobo Optimizer (SABO) and Differential Squirrel Search Algorithm (DSSA) underscores the significance of leveraging the distinctive strengths of each optimizer. The study delves into three variations of the RBF model: RBF, RBDS, and RRBSA. Among these, the RBF model, integrating the SABO optimizer (RBSA), distinguishes itself with an impressive R2 value of 0.995, denoting an exceptionally close alignment with the data. Furthermore, a low Root Mean Square Error (RMSE) value of 0.700 underscores the model's remarkable precision. The research showcases the effectiveness of fusing ML techniques in the RBSA model for precise cooling load predictions. This hybrid model furnishes more dependable insights for energy conservation and sustainable building operations, thereby contributing to a more environmentally conscious and sustainable future.

@article{Wang2024,
  title     = {Enhancing Building Energy Efficiency: A Hybrid Meta-Heuristic Approach for Cooling Load Prediction},
  journal   = {International Journal of Advanced Computer Science and Applications},
  volume    = {15},
  number    = {4},
  year      = {2024},
  publisher = {The Science and Information Organization},
  author    = {Chenguang Wang and Yanjie Zhou and Libin Deng and Ping Xiong and Jiarui Zhang and Jiamin Deng and Zili Lei},
  doi       = {10.14569/IJACSA.2024.0150412},
  url       = {https://doi.org/10.14569/IJACSA.2024.0150412}
}