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Understanding energy demand behaviors through spatio-temporal smart meter data analysis

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  • Niu, Zhibin
  • Wu, Junqi
  • Liu, Xiufeng
  • Huang, Lizhen
  • Nielsen, Per Sieverts

Abstract

Energy demand-side management, especially empowered by the fine-grained smart meter data, plays a significant role in the rational allocation of energy, monitoring and supervision of energy consumption behaviors. Through the in-depth demand analysis including quantification of energy consumption dynamics and consumer preferences, energy decision-makers can develop reasonable and forethoughtful energy efficiency plans and demand-response programs. Previous work in energy-demand behavioral research relied primarily on ideal socio-economic models or data-driven approaches, both of which lack flexibility, intuition and interpretability. This paper proposes a novel spatio-temporal visual analysis approach for urban energy consumption pattern discovery in order to identify energy-saving potentials, plan energy supply and improve energy efficiency. In this approach, energy consumption time series are embeded into a two-dimensional scatterplot for coordinated visual exploration. Users can interactively explore and discover different patterns for decision-making purposes. In addition, we propose the method for modeling energy demand shift patterns based on a potential flow method and integrate it into a pattern exploration tool. The proposed approach is comprehensively evaluated through empirical studies using the real-world electricity consumption data from Pudong district, Shanghai. We identify five typical energy consumption patterns and demand shift patterns across different geographical locations, which can be well interpreted by the knowledge of energy consumption in the area of interest. The results demonstrate the effectiveness of the proposed approach and the tool. This tool can be integrated into smart energy systems for a better understanding of user energy consumption behaviors and preferences.

Suggested Citation

  • Niu, Zhibin & Wu, Junqi & Liu, Xiufeng & Huang, Lizhen & Nielsen, Per Sieverts, 2021. "Understanding energy demand behaviors through spatio-temporal smart meter data analysis," Energy, Elsevier, vol. 226(C).
    • Handle: RePEc:eee:energy:v:226:y:2021:i:c:s0360544221007428
    DOI: 10.1016/j.energy.2021.120493
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    References listed on IDEAS

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    1. José Rubio-León & José Rubio-Cienfuegos & Cristian Vidal-Silva & Jesennia Cárdenas-Cobo & Vannessa Duarte, 2023. "Applying Fuzzy Time Series for Developing Forecasting Electricity Demand Models," Mathematics, MDPI, vol. 11(17), pages 1-18, August.
    2. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
    3. Wu, Junqi & Niu, Zhibin & Li, Xiang & Huang, Lizhen & Nielsen, Per Sieverts & Liu, Xiufeng, 2023. "Understanding multi-scale spatiotemporal energy consumption data: A visual analysis approach," Energy, Elsevier, vol. 263(PD).
    4. Ahammed, Md. Tanvir & Khan, Imran, 2022. "Ensuring power quality and demand-side management through IoT-based smart meters in a developing country," Energy, Elsevier, vol. 250(C).
    5. Gonçalves, Rui & Ribeiro, Vitor Miguel & Pereira, Fernando Lobo, 2023. "Variable Split Convolutional Attention: A novel Deep Learning model applied to the household electric power consumption," Energy, Elsevier, vol. 274(C).
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    7. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
    8. Biemann, Marco & Scheller, Fabian & Liu, Xiufeng & Huang, Lizhen, 2021. "Experimental evaluation of model-free reinforcement learning algorithms for continuous HVAC control," Applied Energy, Elsevier, vol. 298(C).
    9. Haixia Gu & Gaojun Liu & Jixue Li & Hongyun Xie & Hanguan Wen, 2023. "A Framework Based on Deep Learning for Predicting Multiple Safety-Critical Parameter Trends in Nuclear Power Plants," Sustainability, MDPI, vol. 15(7), pages 1-15, April.
    10. Shu, Lei & Mo, Yunjeong & Zhao, Dong, 2024. "Energy retrofits for smart and connected communities: Scopes and technologies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    11. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Cheng, Xu & Chen, Zhe, 2023. "An energy demand-side management and net metering decision framework," Energy, Elsevier, vol. 271(C).
    12. Peng, Jieyang & Kimmig, Andreas & Niu, Zhibin & Wang, Jiahai & Liu, Xiufeng & Ovtcharova, Jivka, 2021. "A flexible potential-flow model based high resolution spatiotemporal energy demand forecasting framework," Applied Energy, Elsevier, vol. 299(C).
    13. Xinyu Dai & Ming Yang & Jipu Wang & Zhihui Xu & Hanguan Wen, 2023. "Human Performance Detection Using Operator Action Log of Nuclear Power Plant," Energies, MDPI, vol. 16(4), pages 1-13, February.
    14. Ahir, Rajesh K. & Chakraborty, Basab, 2023. "A data-driven analytic approach for investigation of electricity demand variability for energy conservation programs," Energy, Elsevier, vol. 282(C).

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