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A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features

Author

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  • 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
    Hebei Key Laboratory of Distributed Energy Storage and Micro-grid (North China Electric Power University), Baoding 071003, China)

  • Kangping Li

    (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)

  • Lihui Jiang

    (China Resources Power Holdings Company Limited, Shenzhen 518001, China)

  • Jianguo Ren

    (China Resources Power Holdings Company Limited, Shenzhen 518001, China)

  • Zengqiang Mi

    (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
    Hebei Key Laboratory of Distributed Energy Storage and Micro-grid (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

Most distributed photovoltaic systems (DPVSs) are normally located behind the meter and are thus invisible to utilities and retailers. The accurate information of the DPVS capacity is very helpful in many aspects. Unfortunately, the capacity information obtained by the existing methods is usually inaccurate due to various reasons, e.g., the existence of unauthorized installations. A two-stage DPVS capacity estimation approach based on support vector machine with customer net load curve features is proposed in this paper. First, several features describing the discrepancy of net load curves between customers with DPVSs and those without are extracted based on the weather status driven characteristic of DPVS output power. A one-class support vector classification (SVC) based DPVS detection (DPVSD) model with the input features extracted above is then established to determine whether a customer has a DPVS or not. Second, a bootstrap-support vector regression (SVR) based DPVS capacity estimation (DPVSCE) model with the input features describing the difference of daily total PV power generation between DPVSs with different capacities is proposed to further estimate the specific capacity of the detected DPVS. A case study using a realistic dataset consisting of 183 residential customers in Austin (TX, U.S.A.) verifies the effectiveness of the proposed approach.

Suggested Citation

  • Fei Wang & Kangping Li & Xinkang Wang & Lihui Jiang & Jianguo Ren & Zengqiang Mi & Miadreza Shafie-khah & João P. S. Catalão, 2018. "A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features," Energies, MDPI, vol. 11(7), pages 1-19, July.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1750-:d:156081
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    References listed on IDEAS

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    1. Fei Wang & Zengqiang Mi & Shi Su & Hongshan Zhao, 2012. "Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters," Energies, MDPI, vol. 5(5), pages 1-16, May.
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    7. Fei Wang & Liming Liu & Yili Yu & Gang Li & Jessica Li & Miadreza Shafie-khah & João P. S. Catalão, 2018. "Impact Analysis of Customized Feedback Interventions on Residential Electricity Load Consumption Behavior for Demand Response," Energies, MDPI, vol. 11(4), pages 1-22, March.
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    Cited by:

    1. Keda Pan & Changhong Xie & Chun Sing Lai & Dongxiao Wang & Loi Lei Lai, 2020. "Photovoltaic Output Power Estimation and Baseline Prediction Approach for a Residential Distribution Network with Behind-the-Meter Systems," Forecasting, MDPI, vol. 2(4), pages 1-18, November.
    2. Taeyoung Kim & Jinho Kim, 2021. "A Regional Day-Ahead Rooftop Photovoltaic Generation Forecasting Model Considering Unauthorized Photovoltaic Installation," Energies, MDPI, vol. 14(14), pages 1-22, July.
    3. Yuan-Kang Wu & Yi-Hui Lai & Cheng-Liang Huang & Nguyen Thi Bich Phuong & Wen-Shan Tan, 2022. "Artificial Intelligence Applications in Estimating Invisible Solar Power Generation," Energies, MDPI, vol. 15(4), pages 1-18, February.
    4. Li, Kangping & Wang, Fei & Mi, Zengqiang & Fotuhi-Firuzabad, Mahmoud & Duić, Neven & Wang, Tieqiang, 2019. "Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    6. Konstantinos Blazakis & Yiannis Katsigiannis & Georgios Stavrakakis, 2022. "One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques," Energies, MDPI, vol. 15(12), pages 1-25, June.
    7. Handrea Bernando Tambunan & Dzikri Firmansyah Hakam & Iswan Prahastono & Anita Pharmatrisanti & Andreas Putro Purnomoadi & Siti Aisyah & Yonny Wicaksono & I Gede Ryan Sandy, 2020. "The Challenges and Opportunities of Renewable Energy Source (RES) Penetration in Indonesia: Case Study of Java-Bali Power System," Energies, MDPI, vol. 13(22), pages 1-22, November.

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