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Long-term Forecasting Heat Use in Sweden's Residential Sector using Genetic Algorithms and Neural Network
Halmstad University, School of Business, Innovation and Sustainability.
Halmstad University, School of Business, Innovation and Sustainability.
2024 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

In this study, the parameters of population, gross domestic product (GDP), heat price, U-value, and temperature have been used to predict heat consumption for Sweden till 2050. It should be noted that the heat consumption has been considered for multi-family houses. Most multi-family houses (MFH) get their primary heat from district heating (DH). A literature analysis of various models and variables has been conducted to enhance comprehension of forecasting and its process. The majority of earlier research has focused on electricity or energy rather than heat. The aim of this study is to create a model (linear and non-linear) from 1993 to 2019 with a minimum error as possible, and then use the genetic algorithm (GA) and neural network (NN) to predict Sweden's heat consumption till 2050

Place, publisher, year, edition, pages
2024. , p. 53
Keywords [en]
Genetic Algorithm, Neural Network, Forecasting, Heat Use
National Category
Energy Systems
Identifiers
URN: urn:nbn:se:hh:diva-52386OAI: oai:DiVA.org:hh-52386DiVA, id: diva2:1826044
Educational program
Master's Programme in Energy smart innovation in the built environment, 120 credits
Presentation
2023-12-13, 13:30 (English)
Supervisors
Examiners
Available from: 2024-01-17 Created: 2024-01-10 Last updated: 2025-10-01Bibliographically approved

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CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf