A deep hybrid prediction framework for building operational carbon emissions: Integrating enhanced extreme learning machines

作者：Jingqi Wang, Yongguang Tan, Junqi Yu, Haifeng Yu, Meng Wang, Meng Zhou · 发表于：Energy Reports · 年份：2025 · DOI：10.1016/j.egyr.2025.03.014 · 被引用次数：10 · 研究领域：Air Quality Monitoring and Forecasting、Energy Load and Power Forecasting、Building Energy and Comfort Optimization

To address the urgent need for reducing carbon emissions in the construction industry, this paper proposes a novel hybrid prediction model, MIV-IHHO-DELM, for forecasting carbon emissions from public building operations. Unlike traditional single prediction models, which often lack accuracy and stability, our model integrates three key innovations: (1) an Improved Harris Hawk Optimization Algorithm (IHHO) enhanced with three strategies to improve high-dimensional optimization capabilities; (2) a Deep Extreme Learning Machine (DELM) optimized by IHHO for initial weight tuning; and (3) the Mean Impact Value (MIV) method for feature selection, which effectively screens multiple parameters from three categories: building energy structure and usage habits, outdoor meteorological conditions, and historical carbon emission data.Through extensive experimental analysis, the proposed MIV-IHHO-DELM model demonstrates superior performance. In ablation experiments, the model achieves a 46.26 % reduction in MAPE and a 37.92 % reduction in RMSE compared to the IHHO-DELM model, and a 71.62 % reduction in MAPE and 73.74 % reduction in RMSE compared to the MIV-DELM model. In comparative experiments, the MIV-IHHO-DELM model achieves a MAPE of 0.704 % and RMSE of 18.0133, outperforming other state-of-the-art prediction models in both accuracy and computational efficiency. Furthermore, the model exhibits strong generalization ability, maintaining high prediction accuracy even with reduced data sa...