Building Energy Efficiency Design and Energy Consumption Analysis Based on MOEA/D Algorithm

Focusing on building energy-saving design has important strategic significance for environmental improvement and reducing energy consumption. Building energy-saving issues are essentially multi-objective optimization problems. The complexity of building systems and the excessive dependence of factors involved make it difficult for traditional design methods to achieve good application results. Therefore, a Multi-objective Evolutionary method based on Decomposition algorithm (MOEA/D) is proposed to incorporate building energy consumption and user discomfort into the building energy efficiency objective function. A multi-agent model and management mechanism under target decomposition is proposed, taking into account computational costs, to better evaluate and predict building energy consumption. Algorithm validation and case analysis were conducted on the designed model. The improved multi-objective algorithm proposed in the study exhibited smaller hypervolume measurement values. The number of uncomfortable hours when solving the objective function was less than 1000, with a total energy consumption of 9.26GJ. In the analysis of building energy efficiency, the proposed algorithm showed an average operating time of less than 2000s. The energy-saving index results were better than other comparative algorithms. The relative prediction error during the cooling and heating seasons was less than 0%, while the maximum prediction error exhibited by traditional methods reached 0.058% and 0.054%, respectively. The energy-saving design idea proposed in the study can effectively analyze building energy consumption, reduce calculation cost, and provide technical references for the optimization design of green building schemes.