Applying Deep Learning to the Heat Production Planning Problem in a District Heating System
District heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems...
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| Format: | Article |
| Language: | English |
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MDPI AG
2020-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/13/24/6641 |
| _version_ | 1797544478707286016 |
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| author | Donghun Lee Seok Mann Yoon Jaeseung Lee Kwanho Kim Sang Hwa Song |
| author_facet | Donghun Lee Seok Mann Yoon Jaeseung Lee Kwanho Kim Sang Hwa Song |
| author_sort | Donghun Lee |
| collection | DOAJ |
| description | District heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems. Optimization-based models provide near optimal solutions, while it takes a while to generate solutions due to the characteristics of the underlying solution mechanism. When prompt re-planning due to any parameter changes is necessary, the traditional approaches might be inefficient to generate modified solutions quickly. In this study, we developed a two-phase solution mechanism, where deep learning algorithm is applied to learn optimal production patterns from optimization module. In the first training phase, the optimization module generates optimal production plans for the input scenarios derived from operations history, which are provided to the deep learning module for training. In the second planning phase, the deep learning module with trained parameters predicts production plan for the test scenarios. The computational experiments show that after the training process is completed, it has the characteristic of quickly deriving results appropriate to the situation. By combining optimization and deep learning modules in a solution framework, it is expected that the proposed algorithm could be applied to online optimization of district heating systems. |
| first_indexed | 2024-03-10T14:01:04Z |
| format | Article |
| id | doaj.art-719bb1dc642843a4a1058e1282cc9ff4 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T14:01:04Z |
| publishDate | 2020-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-719bb1dc642843a4a1058e1282cc9ff42023-11-21T01:04:47ZengMDPI AGEnergies1996-10732020-12-011324664110.3390/en13246641Applying Deep Learning to the Heat Production Planning Problem in a District Heating SystemDonghun Lee0Seok Mann Yoon1Jaeseung Lee2Kwanho Kim3Sang Hwa Song4Industrial and Management Engineering, Incheon National University, Incheon 22012, KoreaKorea District Heating Corporation, Gyeonggi-do 17099, KoreaKorea District Heating Corporation, Gyeonggi-do 17099, KoreaIndustrial and Management Engineering, Incheon National University, Incheon 22012, KoreaGraduate School of Logistics, Incheon National University, Incheon 22012, KoreaDistrict heating system is designed to minimize energy consumption and environmental pollution by employing centralized production facilities connected to demand regions. Traditionally, optimization based algorithms were applied to the heat production planning problem in the district heating systems. Optimization-based models provide near optimal solutions, while it takes a while to generate solutions due to the characteristics of the underlying solution mechanism. When prompt re-planning due to any parameter changes is necessary, the traditional approaches might be inefficient to generate modified solutions quickly. In this study, we developed a two-phase solution mechanism, where deep learning algorithm is applied to learn optimal production patterns from optimization module. In the first training phase, the optimization module generates optimal production plans for the input scenarios derived from operations history, which are provided to the deep learning module for training. In the second planning phase, the deep learning module with trained parameters predicts production plan for the test scenarios. The computational experiments show that after the training process is completed, it has the characteristic of quickly deriving results appropriate to the situation. By combining optimization and deep learning modules in a solution framework, it is expected that the proposed algorithm could be applied to online optimization of district heating systems.https://www.mdpi.com/1996-1073/13/24/6641district heatingoptimizationdeep learningplanningheat production |
| spellingShingle | Donghun Lee Seok Mann Yoon Jaeseung Lee Kwanho Kim Sang Hwa Song Applying Deep Learning to the Heat Production Planning Problem in a District Heating System Energies district heating optimization deep learning planning heat production |
| title | Applying Deep Learning to the Heat Production Planning Problem in a District Heating System |
| title_full | Applying Deep Learning to the Heat Production Planning Problem in a District Heating System |
| title_fullStr | Applying Deep Learning to the Heat Production Planning Problem in a District Heating System |
| title_full_unstemmed | Applying Deep Learning to the Heat Production Planning Problem in a District Heating System |
| title_short | Applying Deep Learning to the Heat Production Planning Problem in a District Heating System |
| title_sort | applying deep learning to the heat production planning problem in a district heating system |
| topic | district heating optimization deep learning planning heat production |
| url | https://www.mdpi.com/1996-1073/13/24/6641 |
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