Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms
Predicting next-day heat load curves is essential to guarantee sufficient heat supply and optimal operation of district heat systems (DHSs). Existing studies have mainly investigated one-step ahead forecasting methods, which can predict a single value at a future time step. To predict heat load curv...
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| Vydáno v: | Energy (Oxford) Ročník 188; s. 116085 |
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| Hlavní autoři: | , , , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
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Oxford
Elsevier Ltd
01.12.2019
Elsevier BV |
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| ISSN: | 0360-5442, 1873-6785 |
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| Abstract | Predicting next-day heat load curves is essential to guarantee sufficient heat supply and optimal operation of district heat systems (DHSs). Existing studies have mainly investigated one-step ahead forecasting methods, which can predict a single value at a future time step. To predict heat load curves, multi-step ahead forecasting methods are needed. This study proposes a machine learning-based framework for multi-step ahead DHS heat load forecasting. Specifically, support vector regression, deep neural network, and extreme gradient boosting (XGBoost) are respectively used as the base learner to develop forecasting models. Two multi-step ahead forecasting methods, i.e. direct strategy and recursive strategy, adopt the learnt models to generate predictions. A DHS in China is used as the case study to comprehensively assess the performance of these two forecasting strategies. Recursive strategy using the XGBoost-based forecasting model can achieve the most accurate and stable predictions with a value of 10.52% for the coefficient of variation of root mean square error. Furthermore, the modeling process of recursive strategy is much more convenient than that of direct strategy. The research shows that the recursive strategy is a better solution to multi-step ahead forecasting than the direct strategy with respect to accuracy, prediction stability, and modeling process.
•A framework for multi-step ahead heat load forecasting is proposed.•Direct and recursive strategies are used to predict daily heat load curves.•Applicability of direct and recursive strategies is assessed from three aspects.•Recursive strategy slightly outperforms direct strategy in accuracy and stability.•Modeling process of recursive strategy is simpler than that of direct strategy. |
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| AbstractList | Predicting next-day heat load curves is essential to guarantee sufficient heat supply and optimal operation of district heat systems (DHSs). Existing studies have mainly investigated one-step ahead forecasting methods, which can predict a single value at a future time step. To predict heat load curves, multi-step ahead forecasting methods are needed. This study proposes a machine learning-based framework for multi-step ahead DHS heat load forecasting. Specifically, support vector regression, deep neural network, and extreme gradient boosting (XGBoost) are respectively used as the base learner to develop forecasting models. Two multi-step ahead forecasting methods, i.e. direct strategy and recursive strategy, adopt the learnt models to generate predictions. A DHS in China is used as the case study to comprehensively assess the performance of these two forecasting strategies. Recursive strategy using the XGBoost-based forecasting model can achieve the most accurate and stable predictions with a value of 10.52% for the coefficient of variation of root mean square error. Furthermore, the modeling process of recursive strategy is much more convenient than that of direct strategy. The research shows that the recursive strategy is a better solution to multi-step ahead forecasting than the direct strategy with respect to accuracy, prediction stability, and modeling process. Predicting next-day heat load curves is essential to guarantee sufficient heat supply and optimal operation of district heat systems (DHSs). Existing studies have mainly investigated one-step ahead forecasting methods, which can predict a single value at a future time step. To predict heat load curves, multi-step ahead forecasting methods are needed. This study proposes a machine learning-based framework for multi-step ahead DHS heat load forecasting. Specifically, support vector regression, deep neural network, and extreme gradient boosting (XGBoost) are respectively used as the base learner to develop forecasting models. Two multi-step ahead forecasting methods, i.e. direct strategy and recursive strategy, adopt the learnt models to generate predictions. A DHS in China is used as the case study to comprehensively assess the performance of these two forecasting strategies. Recursive strategy using the XGBoost-based forecasting model can achieve the most accurate and stable predictions with a value of 10.52% for the coefficient of variation of root mean square error. Furthermore, the modeling process of recursive strategy is much more convenient than that of direct strategy. The research shows that the recursive strategy is a better solution to multi-step ahead forecasting than the direct strategy with respect to accuracy, prediction stability, and modeling process. •A framework for multi-step ahead heat load forecasting is proposed.•Direct and recursive strategies are used to predict daily heat load curves.•Applicability of direct and recursive strategies is assessed from three aspects.•Recursive strategy slightly outperforms direct strategy in accuracy and stability.•Modeling process of recursive strategy is simpler than that of direct strategy. |
| ArticleNumber | 116085 |
| Author | Fang, Xiumu Zhou, Zhigang Chen, Xin Jiang, Yi Xue, Puning Liu, Jing |
| Author_xml | – sequence: 1 givenname: Puning surname: Xue fullname: Xue, Puning organization: School of Architecture, Harbin Institute of Technology, Harbin, 150000, China – sequence: 2 givenname: Yi surname: Jiang fullname: Jiang, Yi email: 350121075@qq.com organization: Heilongjiang Provincial Computing Center, Harbin, 150026, China – sequence: 3 givenname: Zhigang surname: Zhou fullname: Zhou, Zhigang organization: School of Architecture, Harbin Institute of Technology, Harbin, 150000, China – sequence: 4 givenname: Xin surname: Chen fullname: Chen, Xin organization: School of Architecture, Harbin Institute of Technology, Harbin, 150000, China – sequence: 5 givenname: Xiumu surname: Fang fullname: Fang, Xiumu organization: School of Architecture, Harbin Institute of Technology, Harbin, 150000, China – sequence: 6 givenname: Jing surname: Liu fullname: Liu, Jing email: liujinghit0@163.com organization: School of Architecture, Harbin Institute of Technology, Harbin, 150000, China |
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| Keywords | Multi-step ahead forecasting Machine learning algorithms Heat load forecasting District heating Recursive strategy Direct strategy |
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| SubjectTerms | Algorithms Artificial intelligence Artificial neural networks Bluegrass music case studies China Coefficient of variation Direct strategy District heating Forecasting heat Heat load forecasting Heating systems Learning algorithms Machine learning Machine learning algorithms Mathematical models Model accuracy Modelling Multi-step ahead forecasting Neural networks Performance assessment prediction Predictions Recursive functions Recursive methods Recursive strategy regression analysis Strategy Support vector machines |
| Title | Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms |
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