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Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method
Dalarna University, School of Information and Engineering, Microdata Analysis.ORCID iD: 0000-0003-4212-8582
Uppsala University.ORCID iD: 0000-0001-8343-3327
Dalarna University, School of Information and Engineering, Energy Technology.ORCID iD: 0000-0003-3025-6333
Dalarna University, School of Information and Engineering, Energy Technology.ORCID iD: 0000-0002-2369-0169
2022 (English)In: Energy and Built Environment, ISSN 2666-1233Article in journal (Refereed) Published
Abstract [en]

Household electricity demand has substantial impacts on local grid operation, energy storage and the energy performance of buildings. Hourly demand data at district or urban level helps stakeholders understand the demand patterns from a granular time scale and provides robust evidence in energy management. However, such type of data is often expensive and time-consuming to collect, process and integrate. Decisions built upon smart meter data have to deal with challenges of privacy and security in the whole process. Incomplete data due to confidentiality concerns or system failure can further increase the difficulty of modeling and optimization. In addition, methods using historical data to make predictions can largely vary depending on data quality, local building environment, and dynamic factors. Considering these challenges, this paper proposes a statistical method to generate hourly electricity demand data for large-scale single-family buildings by decomposing time series data and recombining them into synthetics. The proposed method used public data to capture seasonality and the distribution of residuals that fulfill statistical characteristics. A reference building was used to provide empirical parameter settings and validations for the studied buildings. An illustrative case in a city of Sweden using only annual total demand was presented for deploying the proposed method. The results showed that the proposed method can mimic reality well and represent a high level of similarity to the real data. The average monthly error for the best month reached 15.9% and the best one was below 10% among 11 tested months. Less than 0.6% improper synthetic values were found in the studied region.

Place, publisher, year, edition, pages
2022.
Keywords [en]
Data generation, Time series decomposition, Hourly electricity demand, Large-scale buildings
National Category
Probability Theory and Statistics
Identifiers
URN: urn:nbn:se:du-39871DOI: 10.1016/j.enbenv.2022.02.011Scopus ID: 2-s2.0-85126005625OAI: oai:DiVA.org:du-39871DiVA, id: diva2:1645371
Funder
Swedish Energy Agency, 46068Available from: 2022-03-17 Created: 2022-03-17 Last updated: 2023-04-14Bibliographically approved

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Han, MengjieHuang, PeiZhang, Xingxing

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Han, MengjieJohari, FatemehHuang, PeiZhang, Xingxing
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CiteExportLink to record
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Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • 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