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Predicting demand in districtheating systems: A neural network approach
Uppsala University, Disciplinary Domain of Science and Technology, Mathematics and Computer Science, Department of Information Technology, Division of Scientific Computing.
2012 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

To run a district heating system as efficiently as possible correct unit-commitmentdecisions has to be made and in order to make those decisions a good forecast ofheat demand for the coming planning period is necessary. With a high quality forecastthe need for backup power and the risk for a too high production are lowered. Thisthesis takes a neural network approach to load forecasting and aims to provide asimple, yet powerful, tool that can provide accurate load forecasts from existingproduction data without the need for extensive model building.The developed software is tested using real life data from two co-generation plantsand the conclusion is that when the quality of the raw data is good, the software canproduce very good forecasting results.

Place, publisher, year, edition, pages
2012. , 43 p.
UPTEC F, ISSN 1401-5757 ; 12015
Keyword [en]
Demand prediction, district heating, neural network
National Category
Computer and Information Science
URN: urn:nbn:se:uu:diva-175082OAI: diva2:530099
External cooperation
EnviLoop AB
Educational program
Master Programme in Engineering Physics
Available from: 2012-06-04 Created: 2012-05-31 Last updated: 2012-06-04Bibliographically approved

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