Outdoor Environment Design Optimization of an Office Building Based on Indoor Thermal Conditions and Building Energy Performance

Abstract

The outdoor environment greatly affects indoor thermal conditions, yet few investigations have been carried out to optimize the outdoor environment physical parameters that lead to improvements in the indoor thermal environment. This paper proposes a method to optimize the pavement albedo, greening rate, and the neighboring building distance for an office building in Shanghai, to improve its indoor thermal conditions and energy performance. Firstly, the Latin hypercube sampling approach was applied to obtain 68 sets of design samples. Secondly, ENVI-met 5.1 coupled with EnergyPlus 23.1.0 was adopted to perform simulations and obtain the discomfort degree hours and building energy consumption. Thirdly, two machine learning prediction algorithms were used to develop discomfort degree hours and energy consumption models, and the artificial neural network models were found to have better prediction performance with R-squares greater than 0.99. Fourthly, the artificial neural network models were used as fitness functions for seven optimization algorithms and Pareto front solutions were obtained during the optimization process. The optimal solutions help to reduce building energy consumption by up to 4.12% and indoor discomfort degree hours by up to 7.45%, as against the reference building. This study contributes to multi-objective outdoor physical design optimization for the indoor thermal environment, comparative analysis of prediction models, and optimization approaches.