Opportunities for Machine Learning in District Heating

被引:16
|
作者
Mbiydzenyuy, Gideon [1 ]
Nowaczyk, Slawomir [2 ]
Knutsson, Hakan [3 ]
Vanhoudt, Dirk [4 ,5 ]
Brage, Jens [6 ]
Calikus, Ece [2 ]
机构
[1] Univ Boras, Dept Informat Technol, SE-50190 Boras, Sweden
[2] Univ Halmstad, CAISR, SE-30118 Halmstad, Sweden
[3] Univ Halmstad, Sch Business Engn & Sci, SE-30118 Halmstad, Sweden
[4] VITO, Boeretang 200, B-2400 Mol, Belgium
[5] EnergyVille, Thor Pk 8310, B-3600 Genk, Belgium
[6] NODA Intelligent Syst, SE-37435 Karlshamn, Sweden
来源
APPLIED SCIENCES-BASEL | 2021年 / 11卷 / 13期
基金
瑞典研究理事会;
关键词
Machine Learning; district heating; review; road-map; research opportunities; FAULT-DETECTION; COOLING SYSTEMS;
D O I
10.3390/app11136112
中图分类号
O6 [化学];
学科分类号
0703 ;
摘要
The district heating (DH) industry is facing an important transformation towards more efficient networks that utilise significantly lower water temperatures to distribute the heat. This change requires taking advantage of new technologies, and Machine Learning (ML) is a popular direction. In the last decade, we have witnessed an extreme growth in the number of published research papers that focus on applying ML techniques to the DH domain. However, based on our experience in the field, and an extensive review of the state-of-the-art, we perceive a mismatch between the most popular research directions, such as forecasting, and the challenges faced by the DH industry. In this work, we present our findings, explain and demonstrate the key gaps between the two communities and suggest a road-map ahead towards increasing the impact of ML research in the DH industry.
引用
收藏
页数:20
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