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Operational Demand Forecasting In District Heating Systems Using Ensembles Of Online Machine Learning Algorithms
NODA, SWE.
NODA, SWE.
EnergyVille, BEL.
EnergyVille, BEL.
Show others and affiliations
2017 (English)In: 15TH INTERNATIONAL SYMPOSIUM ON DISTRICT HEATING AND COOLING (DHC15-2016) / [ed] Ulseth, R, ELSEVIER SCIENCE BV , 2017, 208-216 p.Conference paper, Published paper (Refereed)
Abstract [en]

Heat demand forecasting is in one form or another an integrated part of most optimisation solutions for district heating and cooling (DHC). Since DHC systems are demand driven, the ability to forecast this behaviour becomes an important part of most overall energy efficiency efforts. This paper presents the current status and results from extensive work in the development, implementation and operational service of online machine learning algorithms for demand forecasting. Recent results and experiences are compared to results predicted by previous work done by the authors. The prior work, based mainly on certain decision tree based regression algorithms, is expanded to include other forms of decision tree solutions as well as neural network based approaches. These algorithms are analysed both individually and combined in an ensemble solution. Furthermore, the paper also describes the practical implementation and commissioning of the system in two different operational settings where the data streams are analysed online in real-time. It is shown that the results are in line with expectations based on prior work, and that the demand predictions have a robust behaviour within acceptable error margins. Applications of such predictions in relation to intelligent network controllers for district heating are explored and the initial results of such systems are discussed. (C) 2017 The Authors. Published by Elsevier Ltd.

Place, publisher, year, edition, pages
ELSEVIER SCIENCE BV , 2017. 208-216 p.
Series
Energy Procedia, ISSN 1876-6102 ; 116
Keyword [en]
district heating and cooling networks, heat load forecast, algorithms, machine learning
National Category
Computer Engineering
Identifiers
URN: urn:nbn:se:bth-15085DOI: 10.1016/j.egypro.2017.05.068ISI: 000406743000019OAI: oai:DiVA.org:bth-15085DiVA: diva2:1137318
Conference
15th International Symposium on District Heating and Cooling (DHC), Seoul
Available from: 2017-08-31 Created: 2017-08-31 Last updated: 2017-09-15Bibliographically approved

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Publisher's full texthttp://www.sciencedirect.com/science/article/pii/S1876610217322750?via%3Dihub

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Lavesson, Niklas
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Department of Computer Science and Engineering
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CiteExportLink to record
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Citation style
  • apa
  • harvard1
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  • vancouver
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More styles
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  • Other locale
More languages
Output format
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