Intelligent adjustment and energy consumption optimization of the fresh air system in hospital buildings based on Fuzzy Logic and Genetic Algorithms

Abstract

To improve the intelligent adjustment ability and energy consumption prediction accuracy of the fresh air system in hospital buildings, this study constructs an energy consumption prediction model based on the Back Propagation Neural Network (BPNN). Meanwhile, it introduces the Genetic Algorithm (GA) and Fuzzy Logic Algorithm (FLA) to optimize the BPNN, thus enhancing the model’s global search ability and robustness. By comparing the proposed optimized model with other models, the study analyzes the advantages of the proposed model in terms of prediction accuracy and convergence speed. Moreover, its practical effectiveness in energy consumption and operational cost optimization is evaluated. The results show that the Genetic Algorithm-Fuzzy Logic Algorithm-Back Propagation (GA-FLA-BP) algorithm performs the best in load prediction, with prediction errors typically below 1.5%, particularly on the 5th and 18th days, demonstrating exceptional performance. Compared to the GA-BP and FLA-BP models, the GA-FLA-BP algorithm exhibits stronger capabilities in handling complex data and uncertainty. Regarding energy consumption and electricity cost optimization, GA-FLA-BP also outperforms other models. Its energy consumption prediction accuracy is 91.5% and an electricity cost prediction accuracy is 90.8%, resulting in savings of 29.2% in energy consumption and 31.2% in costs. Although other algorithms show improvements, GA-FLA-BP remains significantly ahead. Furthermore, the GA-FLA-BP algorithm excels in robustness, consistency, time complexity, and real-time performance. This algorithm demonstrates the highest stability and consistency, the fastest processing speed, and the shortest response time, proving its superior performance in energy consumption management and cost optimization. This study enhances the intelligent adjustment capability of the fresh air system in hospital buildings by optimizing the energy consumption prediction model. Therefore, the study significantly reduces energy consumption and operational costs, improving the efficiency and economy of energy management.