IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v11y2018i7p1750-d156081.html
   My bibliography  Save this article

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

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
    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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/11/7/1750/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/11/7/1750/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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.
    2. Yongchun Yang & Xiaodan Wang & Jingjing Luo & Jie Duan & Yajing Gao & Hong Li & Xiangning Xiao, 2017. "Multi-Objective Coordinated Planning of Distributed Generation and AC/DC Hybrid Distribution Networks Based on a Multi-Scenario Technique Considering Timing Characteristics," Energies, MDPI, vol. 10(12), pages 1-29, December.
    3. Luca Massidda & Marino Marrocu, 2017. "Decoupling Weather Influence from User Habits for an Optimal Electric Load Forecast System," Energies, MDPI, vol. 10(12), pages 1-16, December.
    4. Malof, Jordan M. & Bradbury, Kyle & Collins, Leslie M. & Newell, Richard G., 2016. "Automatic detection of solar photovoltaic arrays in high resolution aerial imagery," Applied Energy, Elsevier, vol. 183(C), pages 229-240.
    5. Wang, Fei & Xu, Hanchen & Xu, Ti & Li, Kangping & Shafie-khah, Miadreza & Catalão, João. P.S., 2017. "The values of market-based demand response on improving power system reliability under extreme circumstances," Applied Energy, Elsevier, vol. 193(C), pages 220-231.
    6. Yi Yu & Xishan Wen & Jian Zhao & Zhao Xu & Jiayong Li, 2018. "Co-Planning of Demand Response and Distributed Generators in an Active Distribution Network," Energies, MDPI, vol. 11(2), pages 1-18, February.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. 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.
    2. 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.
    3. Jinling Lu & Bo Wang & Hui Ren & Daqian Zhao & Fei Wang & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
    4. Fei Wang & Zhao Zhen & Chun Liu & Zengqiang Mi & Miadreza Shafie-khah & João P. S. Catalão, 2018. "Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization," Energies, MDPI, vol. 11(1), pages 1-17, January.
    5. 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.
    6. Fei Wang & Lidong Zhou & Hui Ren & Xiaoli Liu, 2017. "Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimizat," Energies, MDPI, vol. 10(12), pages 1-23, November.
    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.
    8. Dongjun Suh & Seongju Chang, 2012. "An Energy and Water Resource Demand Estimation Model for Multi-Family Housing Complexes in Korea," Energies, MDPI, vol. 5(11), pages 1-20, November.
    9. Bin Luo & Shumin Miao & Chuntian Cheng & Yi Lei & Gang Chen & Lang Gao, 2019. "Long-Term Generation Scheduling for Cascade Hydropower Plants Considering Price Correlation between Multiple Markets," Energies, MDPI, vol. 12(12), pages 1-17, June.
    10. Javier López Gómez & Ana Ogando Martínez & Francisco Troncoso Pastoriza & Lara Febrero Garrido & Enrique Granada Álvarez & José Antonio Orosa García, 2020. "Photovoltaic Power Prediction Using Artificial Neural Networks and Numerical Weather Data," Sustainability, MDPI, vol. 12(24), pages 1-18, December.
    11. Mohanty, Sthitapragyan & Patra, Prashanta K. & Sahoo, Sudhansu S. & Mohanty, Asit, 2017. "Forecasting of solar energy with application for a growing economy like India: Survey and implication," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 539-553.
    12. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
    13. Luciano Cavalcante Siebert & Alexandre Rasi Aoki & Germano Lambert-Torres & Nelson Lambert-de-Andrade & Nikolaos G. Paterakis, 2020. "An Agent-Based Approach for the Planning of Distribution Grids as a Socio-Technical System," Energies, MDPI, vol. 13(18), pages 1-13, September.
    14. Tianyu Lu & Hongyu Li, 2024. "Can China’s Regional Industrial Chain Innovation and Reform Policy Make the Impossible Triangle of Energy Attainable? A Causal Inference Study on the Effect of Improving Industrial Chain Resilience," Energies, MDPI, vol. 17(10), pages 1-33, May.
    15. Müller, Jonas & Trutnevyte, Evelina, 2020. "Spatial projections of solar PV installations at subnational level: Accuracy testing of regression models," Applied Energy, Elsevier, vol. 265(C).
    16. Marzouq, Manal & El Fadili, Hakim & Zenkouar, Khalid & Lakhliai, Zakia & Amouzg, Mohammed, 2020. "Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data," Renewable Energy, Elsevier, vol. 157(C), pages 214-231.
    17. Arumugham, Dinesh Rajan & Rajendran, Parvathy, 2021. "Modelling global solar irradiance for any location on earth through regression analysis using high-resolution data," Renewable Energy, Elsevier, vol. 180(C), pages 1114-1123.
    18. Neda Hajibandeh & Mehdi Ehsan & Soodabeh Soleymani & Miadreza Shafie-khah & João P. S. Catalão, 2017. "The Mutual Impact of Demand Response Programs and Renewable Energies: A Survey," Energies, MDPI, vol. 10(9), pages 1-18, September.
    19. Massidda, Luca & Marrocu, Marino, 2023. "Total and thermal load forecasting in residential communities through probabilistic methods and causal machine learning," Applied Energy, Elsevier, vol. 351(C).
    20. Sufyan Samara & Emad Natsheh, 2020. "Intelligent PV Panels Fault Diagnosis Method Based on NARX Network and Linguistic Fuzzy Rule-Based Systems," Sustainability, MDPI, vol. 12(5), pages 1-20, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:11:y:2018:i:7:p:1750-:d:156081. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.