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A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents

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  • Bingtuan Gao

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Xiaofeng Liu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Zhenyu Zhu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

The forecasting of the load profile of the domestic sector is an area of increased concern for the power grid as it appears in many applications, such as grid operations, demand side management, energy trading, and so forth. Accordingly, a bottom-up forecasting framework is presented in this paper based upon bottom level data about the electricity consumption of household appliances. In the proposed framework, a load profile for group households is obtained with a similar day extraction module, household behavior analysis module, and household behavior prediction module. Concretely, similar day extraction module is the core of the prediction and is employed to extract similar historical days by considering the external environmental and household internal influence factors on energy consumption. The household behavior analysis module is used to analyse and formulate the consumption behavior probability of appliances according to the statistical characteristics of appliances’ switch state in historical similar days. Based on the former two modules, household behavior prediction module is responsible for the load profile of group households. Finally, a case study based on the measured data in a practical residential community is performed to illustrate the feasibility and effectiveness of the proposed bottom-up household load forecasting approach.

Suggested Citation

  • Bingtuan Gao & Xiaofeng Liu & Zhenyu Zhu, 2018. "A Bottom-Up Model for Household Load Profile Based on the Consumption Behavior of Residents," Energies, MDPI, vol. 11(8), pages 1-16, August.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:8:p:2112-:d:163603
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    References listed on IDEAS

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    6. Nakyoung Kim & Sangdon Park & Joohyung Lee & Jun Kyun Choi, 2018. "Load Profile Extraction by Mean-Shift Clustering with Sample Pearson Correlation Coefficient Distance," Energies, MDPI, vol. 11(9), pages 1-20, September.

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