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The potential of data exploration methods in identifying the relationship between short-period (daily) water consumption and meteorological factors

Author

Listed:
  • Piasecki Adam
  • Pilarska Agnieszka
  • Golba Radosław

    (Nicolaus Copernicus University in Toruń, Faculty of Earth Sciences and Spatial Management, Toruń, Poland)

Abstract

The purpose of the work was to identify the hidden relationship between water consumption and meteorological factors, using principal component analysis. In addition, clusters of similar days were identified based on relationships identified by k-means. The study was based on data from the city of Toruń (Poland). The analysis was based on daily data from 2014–2017 divided into three groups. Group I included data from the entire period, Group II- from warm half-years (April–September), and Group III-from cold half-years (January–March and October–December). For Groups I and II the extent of water consumption was explained by two principal components. PC1 includes variables that increase water consumption, and PC2 includes variables that lessen water demand. In Group III, water consumption was not linked to any component.

Suggested Citation

  • Piasecki Adam & Pilarska Agnieszka & Golba Radosław, 2021. "The potential of data exploration methods in identifying the relationship between short-period (daily) water consumption and meteorological factors," Bulletin of Geography. Socio-economic Series, Sciendo, vol. 54(54), pages 113-122, December.
    • Handle: RePEc:vrs:buogeo:v:54:y:2021:i:54:p:113-122:n:5
    DOI: 10.2478/bog-2021-0037
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    References listed on IDEAS

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    1. He, Yongxiu & Pang, Yuexia & Zhang, Qi & Jiao, Zhe & Chen, Qian, 2018. "Comprehensive evaluation of regional clean energy development levels based on principal component analysis and rough set theory," Renewable Energy, Elsevier, vol. 122(C), pages 643-653.
    2. Hui Zou & Zhihong Zou & Xiaojing Wang, 2015. "An Enhanced K-Means Algorithm for Water Quality Analysis of The Haihe River in China," IJERPH, MDPI, vol. 12(11), pages 1-14, November.
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