Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement

Yaolin Lin,Wei Yang

doi:10.3389/fenrg.2018.00025

Abstract

Building design following the energy efficiency standards may not achieve the optimal performance in terms of investment cost, energy consumption and thermal comfort. In this paper, an improved multi-objective genetic algorithm (NSGA-II) is combined with building simulation to assist building design optimization for five selected cities located in the hot summer and cold winter region in China. The trade-offs between the annual energy consumption and initial construction cost, as well as between life cycle cost and number of thermal discomfort hours, were explored. Sensitivity analysis of various design parameters on building energy consumption is performed. The optimizations predicted annual energy consumption reduction of 29.08% on average, as compared to a reference building designed following the standard, and 38.6% with 3.18% more cost on the initial investment. New values for a number of building design parameters are recommended for the revision of relevant building energy efficiency standard.

Highlights

The first oil crisis in the 1970s called for the attention of the nations around the world to reduce the energy consumption
This study aims to explore the cost-effective building energy efficiency design optimization with thermal comfort improvement for a residential building in the cold winter/hot summer region in China
The upper part of the graphic is the region of convergence due to cooling temperature control, and the bottom part is due to heating temperature control

Summary

Introduction

The first oil crisis in the 1970s called for the attention of the nations around the world to reduce the energy consumption. Due to the high pressure on energy demand, the developed countries began to study the energy consumption structure. It was found that significant amount of energy was consumed by buildings, where great potential of energy savings was found.

Methods

Results

Conclusion