Scholarlink

<p>Accurately predicting building energy consumption is essential for optimizing energy management, sustainability strategies, and operational efficiency. This study proposes a novel hybrid forecasting model that integrates wavelet decomposition for feature extraction, Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, and Support Vector Regression (SVR) for refined estimates, with all model parameters optimized via a Developed Henry Gas Solubility Optimization (DHGSO) algorithm. The dataset comprises two years of hourly energy consumption data from seven campuses, providing a robust foundation for validation. The proposed method achieves a 20% reduction in RMSE and a 15% reduction in MAPE compared to standalone LSTM and SVR models. This performance demonstrates the benefits of jointly leveraging decomposition-based feature engineering, deep learning, and advanced metaheuristic optimization. The results emphasize the method’s potential for supporting proactive demand response, accurate budget planning, renewable energy integration, and efficient equipment maintenance in large-scale building energy management systems.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Energy consumption prediction in buildings using LSTM and SVR modified by developed Henry gas solubility optimization

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月30日

查看全文
  • Hailu Wan,
  • Gengqiang Huang,
  • Ying Huang,
  • Noradin Ghadimi

摘要

Accurately predicting building energy consumption is essential for optimizing energy management, sustainability strategies, and operational efficiency. This study proposes a novel hybrid forecasting model that integrates wavelet decomposition for feature extraction, Long Short-Term Memory (LSTM) networks for capturing temporal dependencies, and Support Vector Regression (SVR) for refined estimates, with all model parameters optimized via a Developed Henry Gas Solubility Optimization (DHGSO) algorithm. The dataset comprises two years of hourly energy consumption data from seven campuses, providing a robust foundation for validation. The proposed method achieves a 20% reduction in RMSE and a 15% reduction in MAPE compared to standalone LSTM and SVR models. This performance demonstrates the benefits of jointly leveraging decomposition-based feature engineering, deep learning, and advanced metaheuristic optimization. The results emphasize the method’s potential for supporting proactive demand response, accurate budget planning, renewable energy integration, and efficient equipment maintenance in large-scale building energy management systems.