The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences
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Articles | Volume XLIII-B4-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 447–453, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-447-2022
Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLIII-B4-2022, 447–453, 2022
https://doi.org/10.5194/isprs-archives-XLIII-B4-2022-447-2022
 
02 Jun 2022
02 Jun 2022

SMART CAMPUS ELECTRICITY DATA VISUAL ANALYSIS SYSTEM

S. Guo1, C. Jing1, H. Zhang1, X. Lv1, and W. Li2 S. Guo et al.
  • 1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 100044, China
  • 2BGI Engineering Consultants Ltd., Beijing 100038, China

Keywords: Smart Campus, Electricity Data, Data Mining, Visual Analytics, Graphic Design, Visualization System

Abstract. Smart grid is the basic support of smart city development. The application of visual analytics to electricity system helps monitor and analyse the characteristic information in electricity data, which provides a strong guarantee for mastering the operation status of electricity system and achieving effective energy planning. However, as the complexity and size of electricity data continues to grow, it increases the burden on electricity workers to understand and analyse the electricity consumption situation. In response to these problems, a new type of electricity data visualisation and analysis system has been proposed, which enables interactive analysis of large amounts of electricity data. The system has the following advantages. First, A novel visualisation graphic has been designed and implemented to enable electricity workers to visualise a comprehensive picture of electricity consumption at different granularities. Second, designing appropriate visualisations to highlight the characteristics of the data itself, depending on the specific needs and type of data. Finally, the system provides a set of coupled visual views and interactions to support system users to freely explore the campus electricity situation from multiple scales.