Hourly Heat Load Prediction Model Based on Temporal Convolutional Neural Network
Hourly Heat Load Prediction Model Based on Temporal Convolutional Neural Network
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DOI：
10.1109/access.2020.2968536
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发表时间：
2020-01-01
期刊：
影响因子：
3.9
通讯作者：
Li, Han
中科院分区：
文献类型：
--
作者：
Song, Jiancai;Xue, Guixiang;Li, Han
智能集中供热系统（SDHS）是未来实现绿色节能、舒适供暖的重要途径，有利于提高能源利用效率，减少污染排放。供热负荷的准确预测算法在按需供热中起着重要的作用，但供热负荷预测是一个复杂的非线性优化问题，传统预测算法的非线性表达能力差，限制了预测精度。提出了一种基于时间卷积神经网络（TCN）的供热负荷预测模型，该模型综合了卷积神经网络（CNN）的并行特征处理和递归神经网络（RNN）的时域建模能力，实现了复杂数据特征的快速提取。以安阳市4个换热站2018年采暖季的工程数据为例，对基于TCN的预测算法进行了性能评估和验证，并与RFR、ETR、GBR、SVR、NuSVR、SGD、Bagging、Boosting、MLP、RNN、LSTM等先进算法进行了综合比较。仔细分析了。实验结果表明，提出的基于TCN的热负荷预测算法具有性能优越性。
Smart district heating system (SDHS) is an important way to realize green energy saving and comfortable heating in the future, which is conducive to improving energy utilization efficiency and reducing pollution emissions. The accurate prediction algorithm of heating load plays an important role in on-demand heat supply, however, the heating load prediction is a complicated nonlinear optimization problem, and the prediction accuracy is limited due to the poor nonlinear expression ability of the traditional prediction algorithms. This paper proposes a heating load prediction model based on temporal convolutional neural network (TCN), which implements the rapid extraction of complex data features due to the integration of both the parallel feature processing of convolution neural network (CNN) and the time-domain modeling capability of recurrent neural network (RNN). The engineering data of four heat exchange stations located in Anyang, China in the 2018 heating season is used to evaluate and verify the performance of proposed prediction algorithm based on TCN, and the comprehensive comparisons with state-of-the-art algorithms, such as RFR, ETR, GBR, SVR, NuSVR, SGD, Bagging, Boosting, MLP, RNN, LSTM, etc., were analyzed carefully. The experimental results shown that the proposed heat load prediction algorithm based on TCN has performance superiority.