Optimizing passive design strategies for energy efficient buildings using hybrid artificial neural network (ANN) and multi-objective evolutionary algorithm through a case study approach

Abstract

Building energy reduction opportunities and thermal comfort of occupants are two major aspects that could be enhanced through passive design solutions. In tropical weather conditions such as in India, there have been very minimal efforts in developing simulation-based approaches to understand the impacts of common passive design solutions on energy needs and thermal comfort. Therefore, this study focuses on developing an Artificial Neural Network (ANN) and Building Energy Simulation and Optimization (BESO) based framework to improve building energy performance and thermal comfort using passive design solutions in Indian climate condition with mixed-mode operation strategy. The initial part of the study performs building energy simulation through a coupled approach of DesignBuilder and jEPlus to develop a valid ANN surrogate model. Eventually, ANN outputs are integrated with multi-objective evolutionary algorithm (Non-dominated Sorting Genetic Algorithm-II) to find the optimal solutions of predefined passive decision variables of the building for minimizing building energy consumption and maximizing thermal comfort. The optimized results in the study show decrease of 46% of building energy consumption and 7.58% of discomfort hours when the set of passive design solutions are opted. The findings of optimal passive design solutions could be helpful for designers and practitioners in developing energy-efficient buildings.