Analysis of building energy efficiency optimization design effectiveness based on multi-objective optimization algorithm

This research-oriented project focuses on the development and evaluation of a computational framework that integrates multi-objective optimization algorithms with backpropagation (BP) neural network strategies to optimize building energy efficiency and daylighting performance. Traditional architectural design methodologies often struggle to balance conflicting objectives such as minimizing thermal energy consumption while maximizing natural daylighting. To address this limitation, the proposed methodology decomposes the global architectural design problem into distinct, manageable sub-problems, each corresponding to a specific performance objective. A multi-objective optimization framework is then applied to achieve global Pareto-optimal design configurations. The predictive accuracy of the system is enhanced through a trained BP neural network, which models the complex, non-linear relationships between

design parameters and performance metrics. Experimental evaluations demonstrate high forecasting precision, with the energy consumption prediction model achieving near-zero error and the daylighting model exhibiting minimal deviation. The neural network models show strong convergence characteristics within 2800 to 3000 iterations, yielding correlation coefficients exceeding 0.9845. This project provides a structured simulation framework and implementation guidance for evaluating multi-objective optimization in sustainable building design.

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