Coordinated optimization design of buildings and regional integrated energy systems based on load prediction in future climate conditions

Abstract

The influence of global warming on building performance necessitates its consideration during the design phase. The conventional approach of separately designing the buildings and energy systems may not yield the globally optimal solution. To address this issue, this study proposes a coordinated optimization design approach for buildings and regional integrated energy systems (RIES) under future climate conditions. Firstly, a future regional load prediction model based on a Random Forest algorithm optimized by the Grey Wolf Optimizer is established. Next, a bi-level optimization model is proposed, with the upper level optimizing the building envelope to minimize costs and carbon emissions, and the lower level determining the optimal configuration capacity and operating strategy of RIES for cost minimization. Multi-objective grey wolf optimizer and CPLXE are employed to address the problem. Finally, the proposed method is validated using a case study of an office area in Jinan. The results show that the method can reduce costs by 14.6 % and carbon emissions by 50.93 % compared to the prototype building area with independent production systems, highlighting the potential of synergistic design between buildings and RIES. In addition, different optimization results were obtained under typical meteorological year, shared socioeconomic pathway (SSP) 245, and SSP585 climate scenarios, indicating the impact of future climate on building and RIES design. Therefore, it is necessary to consider climate change in future design work.