A comprehensive thermal load forecasting analysis based on machine learning algorithms
Precise forecasting of thermal loads is a critical factor for economic and efficient operation of district heating and cooling networks. If thermal loads are known with high accuracy in advance, use of renewable energies can be maximized, and – in combination with thermal storage units – fossil gene...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-10-01
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| Series: | Energy Reports |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352484721007435 |
| _version_ | 1830191049017917440 |
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| author | Stefan Leiprecht Fabian Behrens Till Faber Matthias Finkenrath |
| author_facet | Stefan Leiprecht Fabian Behrens Till Faber Matthias Finkenrath |
| author_sort | Stefan Leiprecht |
| collection | DOAJ |
| description | Precise forecasting of thermal loads is a critical factor for economic and efficient operation of district heating and cooling networks. If thermal loads are known with high accuracy in advance, use of renewable energies can be maximized, and – in combination with thermal storage units – fossil generation, in particular in peaking units, can be avoided. Machine learning has proven to be a powerful tool for time series forecasting, and has demonstrated significant advancements in recent years. This paper presents the scientific methodology and first results of the publicly funded research project “deepDHC”, which aims at a broad benchmarking of traditional and advanced machine learning methods for thermal load forecasting in district heating and cooling applications. The analysis covers autoregressive forecasting approaches, decision trees such as “adaptive boosting”, but also latest “deep learning” techniques such as the “long short-term memory” (LSTM) neural network. This work is based on data from the district heating network of the city of Ulm in Germany. First, different performance metrics for evaluating forecasting qualities are introduced. Second, approaches for data screening and results of a linear and non-linear correlation analysis are presented. Third, the machine learning tuning process is described. For thermal load forecasting, weather data are key input parameters. This work uses hourly weather forecasts from weather models provided by the German meteorological service. These weather data are updated automatically, and have been statistically corrected in order to represent very accurate point forecasts for up to ten days ahead. In addition, a user-friendly web interface has been developed for use by the district heating network operator. The performance of different machine-learning algorithms is compared based on 72 h heating load forecasts. |
| first_indexed | 2024-12-17T23:31:42Z |
| format | Article |
| id | doaj.art-02762e1c52d04e7c9b1790190eeafae5 |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-12-17T23:31:42Z |
| publishDate | 2021-10-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-02762e1c52d04e7c9b1790190eeafae52022-12-21T21:28:38ZengElsevierEnergy Reports2352-48472021-10-017319326A comprehensive thermal load forecasting analysis based on machine learning algorithmsStefan Leiprecht0Fabian Behrens1Till Faber2Matthias Finkenrath3Kempten University of Applied Sciences, GermanyKempten University of Applied Sciences, GermanyKempten University of Applied Sciences, GermanyCorresponding author.; Kempten University of Applied Sciences, GermanyPrecise forecasting of thermal loads is a critical factor for economic and efficient operation of district heating and cooling networks. If thermal loads are known with high accuracy in advance, use of renewable energies can be maximized, and – in combination with thermal storage units – fossil generation, in particular in peaking units, can be avoided. Machine learning has proven to be a powerful tool for time series forecasting, and has demonstrated significant advancements in recent years. This paper presents the scientific methodology and first results of the publicly funded research project “deepDHC”, which aims at a broad benchmarking of traditional and advanced machine learning methods for thermal load forecasting in district heating and cooling applications. The analysis covers autoregressive forecasting approaches, decision trees such as “adaptive boosting”, but also latest “deep learning” techniques such as the “long short-term memory” (LSTM) neural network. This work is based on data from the district heating network of the city of Ulm in Germany. First, different performance metrics for evaluating forecasting qualities are introduced. Second, approaches for data screening and results of a linear and non-linear correlation analysis are presented. Third, the machine learning tuning process is described. For thermal load forecasting, weather data are key input parameters. This work uses hourly weather forecasts from weather models provided by the German meteorological service. These weather data are updated automatically, and have been statistically corrected in order to represent very accurate point forecasts for up to ten days ahead. In addition, a user-friendly web interface has been developed for use by the district heating network operator. The performance of different machine-learning algorithms is compared based on 72 h heating load forecasts.http://www.sciencedirect.com/science/article/pii/S2352484721007435District heatingLoad forecastingMachine learningDecision treesNeural networksLSTM |
| spellingShingle | Stefan Leiprecht Fabian Behrens Till Faber Matthias Finkenrath A comprehensive thermal load forecasting analysis based on machine learning algorithms Energy Reports District heating Load forecasting Machine learning Decision trees Neural networks LSTM |
| title | A comprehensive thermal load forecasting analysis based on machine learning algorithms |
| title_full | A comprehensive thermal load forecasting analysis based on machine learning algorithms |
| title_fullStr | A comprehensive thermal load forecasting analysis based on machine learning algorithms |
| title_full_unstemmed | A comprehensive thermal load forecasting analysis based on machine learning algorithms |
| title_short | A comprehensive thermal load forecasting analysis based on machine learning algorithms |
| title_sort | comprehensive thermal load forecasting analysis based on machine learning algorithms |
| topic | District heating Load forecasting Machine learning Decision trees Neural networks LSTM |
| url | http://www.sciencedirect.com/science/article/pii/S2352484721007435 |
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