Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach Article Swipe
Junqi Wu
,
Zhibin Niu
,
Xiang Li
,
Lizhen Huang
,
Per Sieverts Nielsen
,
Xiufeng Liu
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.energy.2022.125939
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.1016/j.energy.2022.125939
Understanding energy consumption patterns is crucial for energy demand-side management. Unlike traditional data mining or machine learning-based methods, this paper presents visual analysis methods for exploring energy consumption data from spatial, temporal, and spatiotemporal dimensions, including variability, segmentation, and energy demand shifts. To support the proposed methods, we develop a visual analysis tool that allows users to explore consumption data and validate their hypotheses based on visual results through human–client–server interactions. In particular, we propose a novel potential flow-based method to model energy demand shift patterns and have integrated it into the proposed analysis tool. We comprehensively evaluate the proposed methods and the tool using real-world electricity consumption data from the Shanghai Pudong district, and compare with traditional data mining methods. The results demonstrated the effectiveness and superiority of the proposed visual analysis methods, including its ability to discover the spatiotemporal variability of energy demand, customer groups, and demand shift patterns across different geographical areas and time horizons. All results can be well explained by knowledge of the energy consumption in the study region.
Related Topics
Concepts
Computer science
Energy consumption
Consumption (sociology)
Data mining
Segmentation
Electricity
Scale (ratio)
Energy (signal processing)
Demand patterns
Data science
Artificial intelligence
Machine learning
Demand management
Engineering
Cartography
Geography
Statistics
Mathematics
Sociology
Macroeconomics
Social science
Electrical engineering
Economics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1016/j.energy.2022.125939
- OA Status
- hybrid
- Cited By
- 16
- References
- 65
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4308326656
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4308326656Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.1016/j.energy.2022.125939Digital Object Identifier
- Title
-
Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approachWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2022Year of publication
- Publication date
-
2022-11-04Full publication date if available
- Authors
-
Junqi Wu, Zhibin Niu, Xiang Li, Lizhen Huang, Per Sieverts Nielsen, Xiufeng LiuList of authors in order
- Landing page
-
https://doi.org/10.1016/j.energy.2022.125939Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
hybridOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.1016/j.energy.2022.125939Direct OA link when available
- Concepts
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Computer science, Energy consumption, Consumption (sociology), Data mining, Segmentation, Electricity, Scale (ratio), Energy (signal processing), Demand patterns, Data science, Artificial intelligence, Machine learning, Demand management, Engineering, Cartography, Geography, Statistics, Mathematics, Sociology, Macroeconomics, Social science, Electrical engineering, EconomicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
16Total citation count in OpenAlex
- Citations by year (recent)
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2025: 7, 2024: 4, 2023: 5Per-year citation counts (last 5 years)
- References (count)
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65Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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