Research on Energy-Efficient Building Design Using Target Function Optimization and Genetic Neural Networks

Abstract

OBJECTIVES: This research aims to provide data for decision-makers to achieve sustainability in building construction projects. METHODS: A multi-objective optimization method, using the non-sorting genetic algorithm (NSGA-II), assesses energy efficiency by determining optimal wall types, insulation thickness, and insulation type. This paper utilizes the EnergyPlus API to directly call the simulation engine from within the optimization algorithm. The genetic neural network algorithm iteratively modifies design parameters (e.g., building orientation, insulation levels etc) and evaluates the resulting energy performance using EnergyPlus. RESULTS: This reduces energy consumption and life cycle costs. The framework integrates Matlab-based approaches with traditional simulation tools like EnergyPlus. A data-driven technology compares the framework's effectiveness. CONCLUSION: The study reveals that optimal design configurations can reduce energy consumption by 30% and life cycle costs by 20%, suggesting changes to window fenestration and envelope insulation are necessary. The framework's accuracy and simplicity make it valuable for optimizing building performance.

Keywords

• Building performance
• Cost performance
• Life cycle cost analysis
• Thermal Index
• ANN
• Design variables