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A study on data-driven hybrid heating load prediction methods in low-temperature district heating: An example for nursing homes in Nordic countries
NTNU, Norway.
RISE Research Institutes of Sweden, Digital Systems, Data Science.ORCID iD: 0000-0001-5091-6285
KTH Royal Institute of Technology, Sweden; Uponor AB,Sweden.
Zhejiang University, China.
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2022 (English)In: Energy Conversion and Management, ISSN 0196-8904, E-ISSN 1879-2227, Vol. 269, article id 116163Article in journal (Refereed) Published
Abstract [en]

In the face of green energy initiatives and progressively increasing shares of more energy-efficient buildings, there is a pressing need to transform district heating towards low-temperature district heating. The substantially lowered supply temperature of low-temperature district heating broadens the opportunities and challenges to integrate distributed renewable energy, which requires enhancement on intelligent heating load prediction. Meanwhile, to fulfill the temperature requirements for domestic hot water and space heating, separate energy conversion units on user-side, such as building-sized boosting heat pumps shall be implemented to upgrade the temperature level of the low-temperature district heating network. This study conducted hybrid heating load prediction methods with long-term and short-term prediction, and the main work consisted of four steps: (1) acquisition and processing of district heating data of 20 district heating supplied nursing homes in the Nordic climate (2016–2019); (2) long-term district heating load prediction through linear regression, energy signature curve in hourly resolution, providing an overall view and boundary conditions for the unit sizing; (3) short-term district heating load prediction through two Artificial Neural Network models, f72 and g120, with different prediction input parameters; (4) evaluation of the predicted load profiles based on the measured data. Although the three prediction models met the quality criteria, it was found that including the historical hourly heating loads as the input to the forecasting model enhanced the prediction quality, especially for the peak load and low-mild heating season. Furthermore, a possible application of the heating load profiles was proposed by integrating two building-sized heat pumps in low-temperature district heating, which may be a promising heat supply method in low-temperature district heating. © 2022 The Authors

Place, publisher, year, edition, pages
Elsevier Ltd , 2022. Vol. 269, article id 116163
Keywords [en]
Artificial neural network, District heating load prediction, Linear regression, Low-temperature district heating, Nursing homes
National Category
Anesthesiology and Intensive Care
Identifiers
URN: urn:nbn:se:ri:diva-60081DOI: 10.1016/j.enconman.2022.116163Scopus ID: 2-s2.0-85136538190OAI: oai:DiVA.org:ri-60081DiVA, id: diva2:1694484
Note

Funding details: Energimyndigheten, 51544-1; Funding details: Norges Forskningsråd, 268248; Funding text 1: This article has been written within the research project “Methods for Transparent Energy Planning of Urban Building Stocks– ExPOSe”. The authors gratefully acknowledge the main support from the Research Council of Norway (ExPOSe programme, grant number: 268248) and aided support from the Swedish Energy Agency (grant number: 51544-1). Special thanks go to the Department of Energy and Process Engineering of NTNU and Trondheim Municipality.

Available from: 2022-09-09 Created: 2022-09-09 Last updated: 2023-06-08Bibliographically approved

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Timoudas, Thomas Ohlson

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