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A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets

Author

Listed:
  • Yosui Miyazaki

    (Graduate School of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba 278-8510, Japan)

  • Yusuke Kameda

    (Graduate School of Engineering, Tokyo University of Science, 6-3-1 Niijuku, Katsushika-ku, Tokyo 125-8585, Japan)

  • Junji Kondoh

    (Graduate School of Science and Technology, Tokyo University of Science, 2641 Yamazaki, Noda, Chiba 278-8510, Japan)

Abstract

The number of photovoltaic (PV) power systems being installed worldwide has been increasing. This has resulted in maintenance of an adequate balance between demand and supply becoming a great concern for power system operators. Forecasting PV power outputs is a promising countermeasure that has been garnering significant interest. Conventional methods for achieving this often use learning methods, such as neural networks and support vector regression. In contrast, this paper proposes a short-term power-forecasting method for geographically distributed PV systems that uses only their previous output power data. In the proposed method, first, the ratio of the power generation output to the maximum power output value for each observation instance in a designated period for each PV system at a certain date and time is obtained. Then, the future geographical distribution of the ratio is predicted from the temporal change (motion) of the preceding distribution. Finally, the predicted ratio is reconverted into the power output to perform short-term power forecasting. The results of total PV output power prediction in the Kanto area of Japan indicate that the proposed method has an average mean absolute percentage error of 4.23% and root mean square error of 0.69 kW, which verifies its efficacy.

Suggested Citation

  • Yosui Miyazaki & Yusuke Kameda & Junji Kondoh, 2019. "A Power-Forecasting Method for Geographically Distributed PV Power Systems using Their Previous Datasets," Energies, MDPI, vol. 12(24), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:24:p:4815-:d:299027
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    References listed on IDEAS

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    3. Weiliang Liu & Changliang Liu & Yongjun Lin & Liangyu Ma & Feng Xiong & Jintuo Li, 2018. "Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather," Energies, MDPI, vol. 11(3), pages 1-22, February.
    4. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
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    Cited by:

    1. Taiki Kure & Haruka Danil Tsuchiya & Yusuke Kameda & Hiroki Yamamoto & Daisuke Kodaira & Junji Kondoh, 2022. "Parameter Evaluation in Motion Estimation for Forecasting Multiple Photovoltaic Power Generation," Energies, MDPI, vol. 15(8), pages 1-20, April.
    2. Aleksander Radovan & Viktor Šunde & Danijel Kučak & Željko Ban, 2021. "Solar Irradiance Forecast Based on Cloud Movement Prediction," Energies, MDPI, vol. 14(13), pages 1-25, June.
    3. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2020. "Short-Term Photovoltaic Power Forecasting Using a Convolutional Neural Network–Salp Swarm Algorithm," Energies, MDPI, vol. 13(8), pages 1-20, April.
    4. Hiroki Yamamoto & Junji Kondoh & Daisuke Kodaira, 2022. "Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation," Energies, MDPI, vol. 15(15), pages 1-17, July.
    5. Daisuke Kodaira & Kazuki Tsukazaki & Taiki Kure & Junji Kondoh, 2021. "Improving Forecast Reliability for Geographically Distributed Photovoltaic Generations," Energies, MDPI, vol. 14(21), pages 1-15, November.

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