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A hybrid machine learning approach for the load prediction in the sustainable transition of district heating networks
KTH Royal Institute of Technology, Sweden.
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0001-5091-6285
NTNU Norwegian University of Science and Technology, Norway.
NTNU Norwegian University of Science and Technology, Norway.
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2023 (English)In: Sustainable Cities and Society, E-ISSN 2210-6715, Vol. 90, article id 104892Article in journal (Refereed) Published
Abstract [en]

Current district heating networks are undergoing a sustainable transition towards the 4th and 5th generation of district heating networks, characterized by the integration of different types of renewable energy sources (RES) and low operational temperatures, i.e., 55°C or lower. Due to the lower temperature difference between supply and return, it is necessary to develop novel methods to understand the loads accurately and provide operation scenarios to anticipate demand peaks and increase flexibility in the energy network, both for long- and short-term horizons. In this study, a hybrid machine-learning (ML) method is developed, combining a clustering pre-processing step with a multi-input artificial neural network (ANN) model to predict heat loads in buildings cluster-wise. Specifically, the impact of time-series data clustering, as a pre-processing step, on the performance of ML models was investigated. It was found that data clustering contributes effectively to the reduction of data training costs by limiting the training processes to representative clusters only instead of all datasets. Additionally, low-quality data, including outliers and large measurement gaps, are excluded from the training to enhance the overall prediction performance of the models.

Place, publisher, year, edition, pages
Elsevier, 2023. Vol. 90, article id 104892
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:ri:diva-66693DOI: 10.1016/j.scs.2023.104892OAI: oai:DiVA.org:ri-66693DiVA, id: diva2:1794267
Note

This study is funded by the Swedish Energy Agency (grant number 51544-1), the European Union’s H2020 programme (grant agreement number 101036656), and the Research Council of Norway (grant number 268248). 

Available from: 2023-09-05 Created: 2023-09-05 Last updated: 2023-09-05Bibliographically approved

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Ohlson Timoudas, Thomas

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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
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Output format
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