基于HNN和SDA的超短期热负荷预测研究
Research on ultra-short-term heating load forecasting based on HNN and SDA
摘要：
热负荷的精准预测能够帮助集中供热系统解决其长期运行过程中存在的能源浪费及源荷不匹配问题。在此基础上,本文提出了一种基于混合神经网络(HNN)和相似日法(SDA)的超短期热负荷预测方法,该方法提高了神经网络模型提取数据特征的能力,同时提高了输入训练集的质量。采用山东省某热电联供机组供热侧的供热数据作为案例进行研究,实验结果表明,较单一的卷积神经网络(CNN)、Transformer及长短时记忆(LSTM)神经网络和CNN-LSTM等模型,本文所提出的HNN模型对热负荷预测有更高的精度;同时,SDA的引入提高了神经网络模型的预测精度,缩短了神经网络模型的训练时间。
Abstract：
Accurate forecasting of heating load can help central heating systems solve the problems of energy waste and source-load mismatch in their long-term operation. On this basis, this paper proposes an ultra-short-term heating load forecasting method based on hybrid neural network (HNN) and similar day approach (SDA), which improves the ability of neural network models to extract data features and enhances the quality of input training sets. Using heating data from a combined heat and power (CHP) unit in Shandong Province, the experimental results show that compared with single convolutional neural networks (CNN), Transformers, long short-term memory (LSTM) neural networks, CNN-LSTM and other models, the HNN model proposed in this paper has higher accuracy in heating load forecasting. Meanwhile, the introduction of SDA improves the forecasting accuracy of the neural network model and shortens the training time of the neural network model.
Keywords:central heating; load forecasting; hybrid neural network(HNN); convolutional neural network (CNN); similar day approach