Work

A Study on Improved Grey Wolf Optimizer-BP Neural Network for Building Energy Consumption Prediction

Year: 2025

Type: conference-paper

Abstract: To address the issue of low accuracy in traditional building energy consumption prediction models, this study proposes an improved building energy consumption prediction model that combines an Enhanced Grey Wolf Optimization algorithm (IGWO) with a Back Propagation (BP) neural network. The WOA algorithm was improved by incorporating opposition-based learning, differential evolution algorithm, and introducing a control parameter λ, which effectively resolves the problems of premature convergence and local optima trapping while enhancing algorithm accuracy. The proposed method was validated using operational data from a commercial building complex in Xi'an, China. Empirical results demonstrate that compared to the traditional BP neural network, the IGWO-BP model reduced the Root Mean Square Percentage Error (RMSPE) by 14.1%. Evidently, this model exhibits strong generalization capability, superior prediction accuracy, and reliability, fully satisfying the requirements of practical engineering applications.

DOI: 10.1145/3729706.3729791

Authors: Wenqiang Jing, Zeming Qin, Teng Mua

Institution: Xi'an Eurasia University

Language: English

Topic: Building Energy and Comfort Optimization

Subfield: Building and Construction

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Climate action

Open Access status: gold

Funders: Xi'an Eurasia University, Education Department of Shaanxi Province

Award: G2533261565