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Publikācija: District Heating Demand Short-Term Forecasting

Publication Type Full-text conference paper published in conference proceedings indexed in SCOPUS or WOS database
Funding for basic activity Unknown
Defending: ,
Publication language English (en)
Title in original language District Heating Demand Short-Term Forecasting
Field of research 2. Engineering and technology
Sub-field of research 2.2 Electrical engineering, Electronic engineering, Information and communication engineering
Authors Romāns Petričenko
Kārlis Baltputnis
Antans Sauļus Sauhats
Dmitrijs Soboļevskis
Keywords disctrict heating; forecasting; artificial neural networks; regression
Abstract This paper discusses various forecasting tools that can be used in predicting the thermal load in district heating networks, focusing on day-ahead hourly planning as it is particularly important for cogeneration plants participating in electricity wholesale markets. Forecasts obtained by employing an artificial neural network are compared to a polynomial regression model. Their ability to supplement each other in a combined forecasting tool has been considered as well. Prediction inaccuracy cost is observed and suggested as evaluation criterion. The case studies are based on the district heating network in Riga, Latvia. Recorded data sets of temperature and heat demand are applied for thermal load prediction.
DOI: 10.1109/EEEIC.2017.7977633
Hyperlink: http://ieeexplore.ieee.org/document/7977633/ 
Reference Petričenko, R., Baltputnis, K., Sauhats, A., Soboļevskis, D. District Heating Demand Short-Term Forecasting. In: 2017 IEEE International Conference on Environment and Electrical Engineering and 2017 IEEE Industrial and Commercial Power Systems Europe (EEEIC / I&CPS Europe), Italy, Milan, 6-9 June, 2017. Piscataway, NJ: IEEE, 2017, pp.1374-1378. ISBN 978-1-5386-3918-4. e-ISBN 978-1-5386-3917-7. Available from: doi:10.1109/EEEIC.2017.7977633
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