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BayesForSG:: A Bayesian Model for Forecasting Thermal Load in Smart Grids
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0001-8561-7963
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0003-3489-7429
Luleå University of Technology, Department of Computer Science, Electrical and Space Engineering, Computer Science.ORCID iD: 0000-0002-8681-9572
2016 (English)In: SAC '16: Proceedings of the 31st Annual ACM Symposium on Applied Computing, New York: ACM Digital Library, 2016, p. 2135-2141Conference paper, Published paper (Refereed)
Abstract [en]

Forecasting the thermal load demand for residential buildings assists in optimizing energy production and developing demand response strategies in a smart grid system. However, the presence of a large number of factors such as outdoor temperature, district heating operational parameters, building characteristics and occupant behavior, make thermal load forecasting a challenging task. This paper presents an efficient model for thermal load forecast in buildings with different variations of heat load consumption across both winter and spring seasons using a Bayesian Network. The model has been validated by utilizing the realistic district heating data of three residential buildings from the district heating grid of the city of Skellefteå, Sweden over a period of four months. The results from our model shows that the current heat load consumption and outdoor temperature forecast have the most influence on the heat load forecast. Further, our model outperforms state-of-the-art methods for heat load forecasting by achieving a higher average accuracy of 77.97% by utilizing only 10% of the training data for a forecast horizon of 1 hour.

Place, publisher, year, edition, pages
New York: ACM Digital Library, 2016. p. 2135-2141
Keywords [en]
Smart Grid, smart city, District Heating System, bayesian network, machine learning, forecasting, Information technology - Computer science
Keywords [sv]
Informationsteknik - Datorvetenskap
National Category
Media and Communication Technology
Research subject
Mobile and Pervasive Computing
Identifiers
URN: urn:nbn:se:ltu:diva-30238DOI: 10.1145/2851613.2853127Scopus ID: 2-s2.0-84975788871Local ID: 400b9534-955c-47d1-812d-fe87e9b05c73ISBN: 978-1-4503-3739-7 (print)OAI: oai:DiVA.org:ltu-30238DiVA, id: diva2:1003465
Conference
ACM Symposium on Applied Computing : 04/04/2016 - 07/04/2016
Note
Godkänd; 2016; 20151203 (karan)Available from: 2016-09-30 Created: 2016-09-30 Last updated: 2018-07-10Bibliographically approved

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Saguna, SagunaMitra, KaranÅhlund, Christer

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Citation style
  • apa
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Output format
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