A Multi-Stage Decision Framework for Optimal Energy Efficiency Measures of Educational Buildings: A Case Study of Chongqing

Abstract

Buildings consume large amounts of energy resources and emit considerable amounts of greenhouse gases, especially existing buildings that do not meet energy standards. Building retrofitting is considered one of the most promising and significant solutions to reduce energy consumption and greenhouse gas emissions. However, finding suitable energy efficiency measures for existing buildings is extremely difficult due to the existence of thousands of retrofit measures and the need to meet various objectives. In this paper, a multi-stage decision framework, including a multi-objective optimization model, and a ranking method are proposed to help decision-makers select the optimal energy efficiency measures. The multi-objective optimization model considers the economic and environmental objectives, expressed as the retrofit cost and energy consumption, respectively. The entropy weight ideal point ranking method, an evaluation and ranking method that combines the entropy weight method and ideal point method, is adopted to sort the Pareto front and make a final decision. Then, the proposed decision framework was implemented for the retrofit planning of an educational building in Chongqing, China. The results show that decision-makers can quickly identify near-optimal energy efficiency measures through multi-objective optimization and can select suitable energy efficiency measures using the ranking method. Moreover, energy consumption can be reduced by building retrofitting. The energy consumption of the case building was 64.20 kWh/m2 before retrofitting, and the value can be reduced by 6.79% through retrofitting. Furthermore, the reduction in building energy consumption was significantly improved by applying the decision framework. The highest value of energy consumption was 59.84 kWh/m2, while the lowest value was 27.11 kWh/m2 when implementing the multi-stage decision framework. Thus, this paper provides a useful decision framework for decision-makers to formulate suitable energy efficiency measures.