Renewable energy system optimization of low/zero energy buildings using single-objective and multi-objective optimization methods

Abstract

Low energy buildings and zero energy buildings have attracted increasing attention in both academic and professional fields. The performances of these buildings are largely affected by the design of the renewable energy systems. This paper presents a comparison study on two design optimization methods for renewable energy systems in these buildings, including a single objective optimization using Genetic Algorithm and a multi-objectives optimization using Non-dominated Sorting Genetic Algorithm (NSGA-II). Building energy system models and renewable energy system models are developed and adopted, allowing the consideration of the interaction between building energy systems and renewable energy systems in optimization. Two case studies are conducted to evaluate the capability and effectiveness of proposed optimization methods, based on the Hong Kong Zero Carbon Building. The performances of the buildings with the renewable energy systems optimized by both methods are much better than that of the benchmark building in most scenarios. The single objective optimization can provide the “best” solution directly for a given objective while the multi-objective optimization provides rich information for designers to make better compromised decisions.