District household electricity consumption pattern analysis based on auto-encoder algorithm. Issue 7 (September 2019)

• Record Type: Journal Article
• Authors: Jin, Yuan; Yan, Da; Zhang, Xingxing; Han, Mengjie; Kang, Xuyuan; An, Jingjing; Sun, Hongsan
• Is Part Of: IOP conference series. Volume 609:Issue 7(2019)
• Publication Date: 2019-09
• Subjects: Materials science -- Periodicals; 620.1105
• DOI: 10.1088/1757-899X/609/7/072028
• Languages: English
• ISSNs: 1757-8981

Abstract

Abstract: 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.