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Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications

Author

Listed:
  • Leijiao Ge

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Tianshuo Du

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Changlu Li

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

  • Yuanliang Li

    (Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Jun Yan

    (Concordia Institute for Information Systems Engineering, Concordia University, Montreal, QC H3G 1M8, Canada)

  • Muhammad Umer Rafiq

    (School of Electrical and Information Engineering, Tianjin University, Tianjin 300072, China)

Abstract

In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the shortage of data monitoring devices and the difficulty of comprehensive coverage of measurement equipment has become more significant, bringing great challenges to the efficient management and maintenance of DPVS. Virtual collection is a new DPVS data collection scheme with cost-effectiveness and computational efficiency that meets the needs of distributed energy management but lacks attention and research. To fill the gap in the current research field, this paper provides a comprehensive and systematic review of DPVS virtual collection. We provide a detailed introduction to the process of DPVS virtual collection and identify the challenges faced by virtual collection through problem analogy. Furthermore, in response to the above challenges, this paper summarizes the main methods applicable to virtual collection, including similarity analysis, reference station selection, and PV data inference. Finally, this paper thoroughly discusses the diversified application scenarios of virtual collection, hoping to provide helpful information for the development of the DPVS industry.

Suggested Citation

  • Leijiao Ge & Tianshuo Du & Changlu Li & Yuanliang Li & Jun Yan & Muhammad Umer Rafiq, 2022. "Virtual Collection for Distributed Photovoltaic Data: Challenges, Methodologies, and Applications," Energies, MDPI, vol. 15(23), pages 1-24, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:23:p:8783-:d:980139
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    1. Zhou, Yi & Zhou, Nanrun & Gong, Lihua & Jiang, Minlin, 2020. "Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine," Energy, Elsevier, vol. 204(C).
    2. Ibrahim Salem Jahan & Vojtech Blazek & Stanislav Misak & Vaclav Snasel & Lukas Prokop, 2022. "Forecasting of Power Quality Parameters Based on Meteorological Data in Small-Scale Household Off-Grid Systems," Energies, MDPI, vol. 15(14), pages 1-20, July.
    3. Yusen Wang & Wenlong Liao & Yuqing Chang, 2018. "Gated Recurrent Unit Network-Based Short-Term Photovoltaic Forecasting," Energies, MDPI, vol. 11(8), pages 1-14, August.
    4. Julián Ascencio-Vásquez & Jakob Bevc & Kristjan Reba & Kristijan Brecl & Marko Jankovec & Marko Topič, 2020. "Advanced PV Performance Modelling Based on Different Levels of Irradiance Data Accuracy," Energies, MDPI, vol. 13(9), pages 1-12, May.
    5. Khan, Waqas & Walker, Shalika & Zeiler, Wim, 2022. "Improved solar photovoltaic energy generation forecast using deep learning-based ensemble stacking approach," Energy, Elsevier, vol. 240(C).
    6. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
    7. Yang, Xiuyuan & Xu, Minglu & Xu, Shouchen & Han, Xiaojuan, 2017. "Day-ahead forecasting of photovoltaic output power with similar cloud space fusion based on incomplete historical data mining," Applied Energy, Elsevier, vol. 206(C), pages 683-696.
    8. Corduas, Marcella & Piccolo, Domenico, 2008. "Time series clustering and classification by the autoregressive metric," Computational Statistics & Data Analysis, Elsevier, vol. 52(4), pages 1860-1872, January.
    9. Sunghyeon Choi & Jin Hur, 2020. "An Ensemble Learner-Based Bagging Model Using Past Output Data for Photovoltaic Forecasting," Energies, MDPI, vol. 13(6), pages 1-16, March.
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