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Machine learning-based thermal response time ahead energy demand prediction for building heating systems

机译：基于机器学习的建筑物供热系统热响应时间提前能量需求预测

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摘要

Energy demand prediction of building heating is conducive to optimal control, fault detection and diagnosis and building intelligentization. In this study, energy demand prediction models are developed through machine learning methods, including extreme learning machine, multiple linear regression, support vector regression and backpropagation neural network. Seven different meteorological parameters, operating parameters, time and indoor temperature parameters are used as feature variables of the model. Correlation analysis method is utilized to optimize the feature sets. Moreover, this paper proposes a strategy for obtaining the thermal response time of building, which is used as the time ahead of prediction models. The prediction performances of extreme learning machine models with various hidden layer nodes are analyzed and contrasted. Actual data of building heating using a ground source heat pump system are collected and used to test the performances of the models. Results show that the thermal response time of the building is approximately 40 min. Four feature sets are obtained, and the performances of the models with feature set 4 are better. For different machine learning methods, the performances of extreme learning machine models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the extreme learning machine model with feature set 4.
机译：建筑物供暖的能量需求预测有助于优化控制，故障检测和诊断以及建筑物智能化。在这项研究中，能源需求预测模型是通过机器学习方法开发的，包括极限学习机，多元线性回归，支持向量回归和反向传播神经网络。七个不同的气象参数，运行参数，时间和室内温度参数用作模型的特征变量。相关分析方法被用来优化特征集。此外，本文提出了一种获取建筑物热响应时间的策略，该策略用作预测模型之前的时间。分析并对比了具有各种隐藏层节点的极限学习机模型的预测性能。收集使用地源热泵系统的建筑物供热的实际数据，并用于测试模型的性能。结果表明，建筑物的热响应时间约为40分钟。获得了四个特征集，具有特征集4的模型的性能更好。对于不同的机器学习方法，极限学习机器模型的性能要优于其他机器模型。此外，对于功能集为4的极限学习机模型，隐藏层节点的最佳数量为11。

著录项

  • 来源
    《Applied Energy》 |2018年第1期|16-27|共12页
  • 作者

    Guo Yabin; Wang Jiangyu; Chen Huanxin; Li Guannan; Liu Jiangyan; Xu Chengliang; Huang Ronggeng; Huang Yao;

  • 作者单位

    Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Dept Refrigerat & Cryogen, Wuhan, Hubei, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Dept Refrigerat & Cryogen, Wuhan, Hubei, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Dept Refrigerat & Cryogen, Wuhan, Hubei, Peoples R China;

    Wuhan Univ Sci & Technol, Sch Urban Construct, Wuhan, Hubei, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Dept Refrigerat & Cryogen, Wuhan, Hubei, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Dept Refrigerat & Cryogen, Wuhan, Hubei, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Dept Refrigerat & Cryogen, Wuhan, Hubei, Peoples R China;

    Huazhong Univ Sci & Technol, Sch Energy & Power Engn, Dept Refrigerat & Cryogen, Wuhan, Hubei, Peoples R China;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Energy demand prediction; Building heating system; Machine learning; Thermal response time; Extreme learning machine;

    机译：能源需求预测;建筑供暖系统;机器学习;热响应时间;极限学习机;

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