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Deep Neural Network-based Impacts Analysis of Multimodal Factors on Heat Demand Prediction
Beijing University of Posts and Telecommunications, Beijing, China.
Beijing University of Posts and Telecommunications, Beijing, China.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-6279-4446
Shandong University, 12589 Jinan, Shandong China.
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(English)In: IEEE Transactions on Big Data, ISSN 2372-2096Article in journal (Refereed) Epub ahead of print
Abstract [en]

Prediction of heat demand using artificial neural networks has attracted enormous research attention. Weather conditions, such as direct solar irradiance and wind speed, have been identified as key parameters affecting heat demand. This paper employs an Elman neural network to investigate the impacts of direct solar irradiance and wind speed on the heat demand from the perspective of the entire district heating network. Results of the overall mean absolute percentage error (MAPE) show that direct solar irradiance and wind speed have quite similar impacts. However, the involvement of direct solar irradiance can clearly reduce the maximum absolute deviation when only involving direct solar irradiance and wind speed, respectively. In addition, the simultaneous involvement of both wind speed and direct solar irradiance does not show an obvious improvement of MAPE. Moreover, the prediction accuracy can also be affected by other factors like data discontinuity and outliers.

Place, publisher, year, edition, pages
IEEE.
Keywords [en]
District heating, deep learning, Elman neural network, heat demand, direct solar irradiance, wind speed
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-46991DOI: 10.1109/TBDATA.2019.2907127OAI: oai:DiVA.org:mdh-46991DiVA, id: diva2:1392236
Available from: 2020-02-06 Created: 2020-02-06 Last updated: 2020-02-24Bibliographically approved

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