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- Han, Mengjie,1985-Högskolan Dalarna,Mikrodataanalys,Dalarna University (author)
Generating hourly electricity demand data for large-scale single-family buildings by a decomposition-recombination method
- Article/chapterEnglish2022
Publisher, publication year, extent ...
- Elsevier BV,2022
- printrdacarrier
Numbers
- LIBRIS-ID:oai:DiVA.org:du-39871
- https://urn.kb.se/resolve?urn=urn:nbn:se:du-39871URI
- https://doi.org/10.1016/j.enbenv.2022.02.011DOI
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-468707URI
Supplementary language notes
- Language:English
- Summary in:English
Part of subdatabase
- SwePubSwePub
Classification
Notes
- 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.
Subject headings and genre
- NATURVETENSKAP Matematik Sannolikhetsteori och statistik hsv//swe
- NATURAL SCIENCES Mathematics Probability Theory and Statistics hsv//eng
- TEKNIK OCH TEKNOLOGIER Naturresursteknik Energisystem hsv//swe
- ENGINEERING AND TECHNOLOGY Environmental Engineering Energy Systems hsv//eng
- Data generation
- Time series decomposition
- Hourly electricity demand
- Large-scale buildings
Added entries (persons, corporate bodies, meetings, titles ...)
- Johari, FatemehUppsala universitet,Byggteknik och byggd miljö,Uppsala University(Swepub:uu)fatjo876 (author)
- Huang, PeiHögskolan Dalarna,Energiteknik,Dalarna University(Swepub:du)phn (author)
- Zhang, XingxingHögskolan Dalarna,Energiteknik,Dalarna University(Swepub:uu)xinzh859 (author)
- Högskolan DalarnaMikrodataanalys (creator_code:org_t)
Related titles
- In:Energy and Built Environment: Elsevier BV2666-1233
Internet link
- https://doi.org/10.1016/j.enbenv.2022.02.011Fulltext
- https://doi.org/10.1016/j.enbenv.2022.02.011
- https://uu.diva-portal.org/smash/get/diva2:1641051/FULLTEXT01.pdffulltext:print
- https://urn.kb.se/resolve?urn=urn:nbn:se:du-39871
- https://doi.org/10.1016/j.enbenv.2022.02.011
- https://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-468707
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