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A thermal response time ahead energy demand prediction strategy for building heating system using machine learning methods

机译：使用机器学习方法建筑加热系统的能量需求预测策略的热响应时间

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Energy demand prediction of building heating is conducive to optimal control, fault detection and diagnosis and building intelligent. In this paper, the prediction models are developed using machine learning methods including extreme learning machine (ELM), multiple linear regression, support vector regression and BP neural network. The feature variable sets are optimized through correlation analysis and supplementing indoor temperature. Besides, this paper proposed a strategy to determine the time ahead of prediction model. The thermal response time of building is used as the prediction time step of model. The prediction performances of ELM models with different hidden layer nodes are analyzed and contrasted. The actual data of the building heating using ground source heat pump system are collected and used to test the performances of the models. The results show that the thermal response time of the building is about 40 minutes. Four feature sets are obtained and performances of models with FS4 are better. For different machine learning methods, the performances of ELM models are better than others. In addition, the optimal number of hidden layer nodes is 11 for the ELM model with FS4.
机译：建筑加热的能源需求预测有利于最优控制，故障检测和诊断和建筑智能化。在本文中，使用包括极端学习机（ELM），多元线性回归，支持向量回归和BP神经网络的机器学习方法开发了预测模型。通过相关性分析和补充室内温度来优化特征变量集。此外，本文提出了一种确定预测模型的时间的策略。建筑物的热响应时间用作模型的预测时间步骤。分析了不同隐藏层节点的ELM模型的预测性能，并对比。收集使用地源热泵系统的建筑加热的实际数据，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，并且具有FS4的模型的性能更好。对于不同的机器学习方法，ELM模型的性能比其他机器更好。此外，对于具有FS4的ELM模型，隐藏层节点的最佳数量为11。

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