NSGA-II and fuzzy comprehensive evaluation-based optimal strategy for dynamic energy optimization considering local thermal sensation

Creating an energy-efficient and comfortable indoor environment is particularly important, as people spend most of their time indoors. Previous studies mainly focused on optimizing energy consumption based on overall thermal comfort, which may lead to local thermal discomfort. To save energy while ensuring local thermal comfort, this study proposes a dynamic energy optimization strategy based on Non-dominated Sorting Genetic Algorithm II (NSGA-II) and fuzzy comprehensive evaluation to optimize supply air parameters. Firstly, the manikin model is constructed from six local body parts, including the head, body, arm, thigh, shank and foot. Following this, the response surface models of the ventilation performance are developed based on CFD simulation data verified by experiments. Subsequently, the supply air parameters are optimized using Pareto-based NSGA-II to obtain the Pareto-optimal solutions set under different outdoor temperatures. Finally, the optimization strategy for dynamic outdoor air temperatures is further improved through the fuzzy comprehensive evaluation method, which significantly enhances ventilation performance. The results indicate that optimizing ventilation performance considering local thermal comfort can effectively prevent thermal discomfort caused by factors such as thermal buoyancy and airflow. The optimized overall predicted mean vote (PMV) and local PMVs are reduced by an average of 54.8% and 58.8%, respectively, with little difference in energy consumption. Moreover, when the outdoor temperature changes dynamically, the proposed dynamic optimization strategy avoids local thermal discomfort and reduces energy consumption by an average of 2.39%.