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District household electricity consumption pattern analysis based on auto-encoder algorithm
Dalarna University, School of Technology and Business Studies, Energy Technology.ORCID iD: 0000-0002-2369-0169
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0003-4212-8582
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2019 (English)In: IOP Conference Series: Materials Science and Engineering, 2019, Vol. 609, no 7, article id 072028Conference paper, Published paper (Refereed)
Abstract [en]

The energy shortage is one key issue for sustainable development, a potential solution of which is the integration with the renewable energy resources. However, the temporal sequential characteristic of renewable resources is different from traditional power grid. For the entire power grid, it is essential to match the energy generation side with the energy consumption side, so the load characteristic at the energy use side is crucial for renewable power integration. Better understanding of energy consumption pattern in buildings contributes to matching different source of energy generation. Under the background of integration of traditional and renewable energy, this research focuses on analysis of different household electricity consumption patterns in an urban scale. The original data is from measurement of daily energy consumption with smart meter in households. To avoid the dimension explosion phenomenon, the auto-encoder algorithm is introduced during the clustering analysis of daily electricity use data, which plays the role of principal component analysis. The clustering based on auto-encoder gives a clear insight into the urban electricity use patterns in household. During the data analysis, several feature variables are proposed, which include peak value, valley value and average value. The distinction analysis is also conducted to evaluate the analysis performance. The study takes households in Nanjing city, China as a case study, to conduct the clustering analysis on electricity consumption of residential buildings. The analysis results can be further applied, such as during the capacity design of district energy storage.

Place, publisher, year, edition, pages
2019. Vol. 609, no 7, article id 072028
Series
IOP Conference Series: Materials Science and Engineering, ISSN 17578981
National Category
Civil Engineering
Research subject
Energy and Built Environments
Identifiers
URN: urn:nbn:se:du-31169DOI: 10.1088/1757-899X/609/7/072028Scopus ID: 2-s2.0-85074698362OAI: oai:DiVA.org:du-31169DiVA, id: diva2:1375916
Conference
10th International Conference on Indoor Air Quality, Ventilation and Energy Conservation in Buildings, IAQVEC 2019; Bari; Italy; 5 September 2019 through 7 September 2019; Code 153083
Available from: 2019-12-06 Created: 2019-12-06 Last updated: 2019-12-06

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Zhang, XingxingHan, Mengjie

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