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Data-Driven Predictions of Heating Energy Savings in Residential Buildings
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Mechanics, Byggteknik.
Uppsala University, Disciplinary Domain of Science and Technology, Technology, Department of Engineering Sciences, Applied Mechanics, Byggteknik.
2019 (English)Independent thesis Advanced level (professional degree), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

Along with the increasing use of intermittent electricity sources, such as wind and sun, comes a growing demand for user flexibility. This has paved the way for a new market of services that provide electricity customers with energy saving solutions. These include a variety of techniques ranging from sophisticated control of the customers’ home equipment to information on how to adjust their consumption behavior in order to save energy. This master thesis work contributes further to this field by investigating an additional incentive; predictions of future energy savings related to indoor temperature. Five different machine learning models have been tuned and used to predict monthly heating energy consumption for a given set of homes. The model tuning process and performance evaluation were performed using 10-fold cross validation. The best performing model was then used to predict how much heating energy each individual household could save by decreasing their indoor temperature by 1°C during the heating season. The highest prediction accuracy (of about 78%) is achieved with support vector regression (SVR), closely followed by neural networks (NN). The simpler regression models that have been implemented are, however, not far behind. According to the SVR model, the average household is expected to lower their heating energy consumption by approximately 3% if the indoor temperature is decreased by 1°C. 

Place, publisher, year, edition, pages
2019. , p. 62
Series
UPTEC STS, ISSN 1650-8319 ; 19027
Keywords [en]
Building Energy, Machine Learning, Energy Savings, Heating Energy, Indoor Temperature, Neural Networks, Support Vector Regression, Random Forest, Ridge Regression, K-Nearest Neighbors
National Category
Energy Systems Computer and Information Sciences
Identifiers
URN: urn:nbn:se:uu:diva-387395OAI: oai:DiVA.org:uu-387395DiVA, id: diva2:1329457
External cooperation
Tibber AS
Educational program
Systems in Technology and Society Programme
Supervisors
Examiners
Available from: 2019-06-24 Created: 2019-06-24 Last updated: 2019-06-24Bibliographically approved

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CiteExportLink to record
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Citation style
  • apa
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