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Forecasting the aggregated output of a large fleet of small behind-the-meter solar photovoltaic sites

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  • Shaker, Hamid
  • Manfre, Daniel
  • Zareipour, Hamidreza

Abstract

Significant growth of behind-the-meter solar Photovoltaic (PV) power generation in recent years is changing the shape of the net demand for electricity from electrical grids. In this work, a framework is proposed to forecast the aggregated power generation of a large fleet of small behind-the-meter solar PV sites. The outputs of those sites are not individually measured and thus, the aggregated output is “invisible” to power system operators. The proposed model uses the available historical power generation data of a very limited number of representative sites in the region, along with Numerical Weather Predictions (NWP) inputs. This way, it is not necessary to constantly monitor all the sites in the region. Fuzzy Arithmetic Wavelet Neural Networks (FAWNN) are used to develop the forecasting engine, providing fuzzy confidence intervals for any desired level, so this methodology can handle various shapes of uncertainties in the input data. The proposed model is validated using actual PV generation data from 6673 sites in California. The simulation results have shown that the proposed approach is capable of forecasting BTM solar PV fleet despite using limited data. The root mean squared error for the forecasts was found to be 3% for the California region.

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  • Shaker, Hamid & Manfre, Daniel & Zareipour, Hamidreza, 2020. "Forecasting the aggregated output of a large fleet of small behind-the-meter solar photovoltaic sites," Renewable Energy, Elsevier, vol. 147(P1), pages 1861-1869.
    • Handle: RePEc:eee:renene:v:147:y:2020:i:p1:p:1861-1869
    DOI: 10.1016/j.renene.2019.09.102
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    1. Nobre, André M. & Severiano, Carlos A. & Karthik, Shravan & Kubis, Marek & Zhao, Lu & Martins, Fernando R. & Pereira, Enio B. & Rüther, Ricardo & Reindl, Thomas, 2016. "PV power conversion and short-term forecasting in a tropical, densely-built environment in Singapore," Renewable Energy, Elsevier, vol. 94(C), pages 496-509.
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    2. 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.
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    5. Qu, Zhijian & Xu, Juan & Wang, Zixiao & Chi, Rui & Liu, Hanxin, 2021. "Prediction of electricity generation from a combined cycle power plant based on a stacking ensemble and its hyperparameter optimization with a grid-search method," Energy, Elsevier, vol. 227(C).
    6. Dengchang Ma & Rongyi Xie & Guobing Pan & Zongxu Zuo & Lidong Chu & Jing Ouyang, 2023. "Photovoltaic Power Output Prediction Based on TabNet for Regional Distributed Photovoltaic Stations Group," Energies, MDPI, vol. 16(15), pages 1-22, July.
    7. Wen, Haoran & Du, Yang & Chen, Xiaoyang & Lim, Eng Gee & Wen, Huiqing & Yan, Ke, 2023. "A regional solar forecasting approach using generative adversarial networks with solar irradiance maps," Renewable Energy, Elsevier, vol. 216(C).
    8. Hidayatno, Akhmad & Setiawan, Andri D. & Wikananda Supartha, I Made & Moeis, Armand O. & Rahman, Irvanu & Widiono, Eddie, 2020. "Investigating policies on improving household rooftop photovoltaics adoption in Indonesia," Renewable Energy, Elsevier, vol. 156(C), pages 731-742.
    9. Qiu, Lihong & Ma, Wentao & Feng, Xiaoyang & Dai, Jiahui & Dong, Yuzhuo & Duan, Jiandong & Chen, Badong, 2024. "A hybrid PV cluster power prediction model using BLS with GMCC and error correction via RVM considering an improved statistical upscaling technique," Applied Energy, Elsevier, vol. 359(C).
    10. 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.
    11. 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).
    12. Marco Pierro & Fabio Romano Liolli & Damiano Gentili & Marcello Petitta & Richard Perez & David Moser & Cristina Cornaro, 2022. "Impact of PV/Wind Forecast Accuracy and National Transmission Grid Reinforcement on the Italian Electric System," Energies, MDPI, vol. 15(23), pages 1-28, November.
    13. Rong-Jong Wai & Pin-Xian Lai, 2022. "Design of Intelligent Solar PV Power Generation Forecasting Mechanism Combined with Weather Information under Lack of Real-Time Power Generation Data," Energies, MDPI, vol. 15(10), pages 1-30, May.
    14. Ji-Won Cha & Sung-Kwan Joo, 2021. "Probabilistic Short-Term Load Forecasting Incorporating Behind-the-Meter (BTM) Photovoltaic (PV) Generation and Battery Energy Storage Systems (BESSs)," Energies, MDPI, vol. 14(21), pages 1-19, October.

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