Development of an intelligent building controller to mitigate indoor thermal dissatisfaction and peak energy demands in a district heating system

Abstract District heating systems were gradually improved with the development of generation, storage, distribution technologies, and the demands continued to expand significantly. The percentage of houses supplied by district heating systems were fast grown up, and it was reported that the global market for the systems would expand by about 6% in the period between 2016 and 2024. However, most studies for district heating models focused on fuel use in plants, energy distribution, and carbon reduction. Many simulations adopting computing technologies dealt with mechanical performances in the systems. Also, recent statistical analyses overlooked zone-scaled thermal comfort directly affecting users' workability in buildings. This research proposes an intelligent controller to improve thermal comfort and reduce peak energy demands in a district heating system. An artificial intelligence based model with temperature and thermal comfort detectors optimizes supply air conditions to maintain desired room temperature responding to users' characteristics in four different building types. The model reduces peak demands for cooling and heating to optimize plant and distribution capacity. Comparative analyses describe the model's effectiveness that it improves thermal comfort level by 27%, and that it reduces peak energy demands by 30% in comparison with a conventional on/off controller. The model has an advantage that it properly responds to temperature changes with high performance to mitigate thermal dissatisfaction and energy loss. In spite of the sensitive controls to ensure human comfort, it is confirmed that the model can contribute to design optimization for energy supply systems in urban scaled models.