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Analysis of Key Factors in Heat Demand Prediction with Neural Networks
Beijing University of Posts and Telecommunications, China.
Mälardalen University, School of Business, Society and Engineering, Future Energy Center.ORCID iD: 0000-0002-6279-4446
Beijing University of Posts and Telecommunications, China..
Shandong University, China.
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2017 (English)In: Energy Procedia, ISSN 1876-6102, E-ISSN 1876-6102, Vol. 105, p. 2965-2970Article in journal (Refereed) Published
Abstract [sv]

The development of heat metering has promoted the development of statistic models for the prediction of heat demand, due to the large amount of available data, or big data. Weather data have been commonly used as input in such statistic models. In order to understand the impacts of direct solar radiance and wind speed on the model performance comprehensively, a model based on Elman neural networks (ENN) was adopted, of which the results can help heat producers to optimize their production and thus mitigate costs. Compared with the measured heat demand, the introduction of wind speed and direct solar radiation has opposite impacts on the performance of ENN and the inclusion of wind speed can improve the prediction accuracy of ENN. However, ENN cannot benefit from the introduction of both wind speed and direct solar radiation simultaneously. 

Place, publisher, year, edition, pages
2017. Vol. 105, p. 2965-2970
National Category
Energy Engineering
Identifiers
URN: urn:nbn:se:mdh:diva-37560DOI: 10.1016/j.egypro.2017.03.704Scopus ID: 2-s2.0-85020715688OAI: oai:DiVA.org:mdh-37560DiVA, id: diva2:1169133
Conference
8th International Conference on Applied Energy, ICAE 2016, 8 October 2016 through 11 October 2016
Available from: 2017-12-22 Created: 2017-12-22 Last updated: 2018-01-02Bibliographically approved

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