Prediction of thermal comfort indoors and cooling loads based on reasonable zoning using the improved HHO with multi-strategy fusion-FENN algorithm

Abstract

A comfortable indoor thermal environment helps occupants work healthily and efficiently, and the air-conditioning system, as the most energy-consuming system in building operation, accurate energy modeling while balancing thermal comfort can provide effective control strategies for managers. Metaheuristic algorithms are widely optimized for various types of machine learning models for the study of data-driven. However, the majority of them currently use the default hyperparameter or random and grid search, which are prone to obtaining inaccurate prediction results. In this paper, a sizable commercial building in Shaanxi Province is used as a research sample. The regional per floor firstly is divided with business model distribution, the cross-validation of random forest and correlation analysis is used to extract features, and the input factors of the prediction model are decided, and then the fully elman neural network is optimized using a segmental fusion strategy combining Cauchy operator and reverse learning to promote harris hawks optimization algorithm, and the prediction model the IMFHHO-FENN (improved Harris Hawks Optimization with multi-strategy fusion-Fully Elman Neural Network) is trained and constructed, which is finally used for predicting thermal comfort and cooling load. According to verification results, it can be proved that the IMFHHO-FENN model has excellent performance compared to mainstream single and combined models and can provide accurate results of predicted models in the design phase of building energy efficiency and later operation management, reducing energy consumption waste while satisfying personnel thermal comfort as much as possible and supply marked theoretical and decision reference for building equipment management.