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Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression

Author

Listed:
  • Fei Wang

    (State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources, North China Electric Power University, Baoding 071003, China
    Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China)

  • Yili Yu

    (Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China)

  • Xinkang Wang

    (Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China)

  • Hui Ren

    (Department of Electrical Engineering, North China Electric Power University, Baoding 071003, China)

  • Miadreza Shafie-Khah

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal)

  • João P. S. Catalão

    (C-MAST, University of Beira Interior, 6201-001 Covilhã, Portugal
    INESC TEC and the Faculty of Engineering of the University of Porto, 4200-465 Porto, Portugal
    INESC-ID, Instituto Superior Técnico, University of Lisbon, 1049-001 Lisbon, Portugal)

Abstract

This paper aims to identity the significant impact factors (IFs) of the residential electricity consumption level (RECL) and to better understand the influence mechanism of IFs on RECL. The analysis of influence mechanism is commonly through regression model where feature selection must first be performed to pick out non-redundant IFs that is highly correlated with RECL. In contrast to the existing studies, this study recognizes the problem that majority feature selection methods (e.g., step regression) are limited to the identification of linear relationships and proposes a novel wrapper feature selection (WFS) method to address this issue. The WFS is based on genetic algorithm (GA) and multinomial logistic regression (MLR). GA is a searching algorithm used to generate different feature subsets (FSs) that consist of several IFs. MLR is a modeling algorithm used to score these FSs. Further, maximal information coefficient (MIC) is utilized to verify the validity of WFS for selecting IFs. Finally, MLR based explanatory model is established to excavate the relationship between selected IFs and RECL. The results of Ireland dataset based case study show that WFS can identify the significant and non-redundant IFs that are linearly or nonlinearly related to RECL. The details about how selected IFs affect RECL are also provided via the explanatory model. Such research can provide useful guidance for a wide range of stakeholders including local governments, electric power companies, and individual households.

Suggested Citation

  • Fei Wang & Yili Yu & Xinkang Wang & Hui Ren & Miadreza Shafie-Khah & João P. S. Catalão, 2018. "Residential Electricity Consumption Level Impact Factor Analysis Based on Wrapper Feature Selection and Multinomial Logistic Regression," Energies, MDPI, vol. 11(5), pages 1-26, May.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:5:p:1180-:d:145117
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    References listed on IDEAS

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