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Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems

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  • Kuk Yeol Bae

    (Energy ICT·ESS Laboratory, Korea Institute of Energy Research, 152 Gajeong-ro, Yuseong-gu, Daejeon 34129, Korea)

  • Han Seung Jang

    (Shcool of Electrical, Electronic Communication, and Computer Engineering, Chonnam National University, 50, Daehak-ro, Yeosu 59626, Korea)

  • Bang Chul Jung

    (Department of Electronics Engineering, Chungnam National University, 99 Daehak-ro, Yuseong-gu, Daejeon 34134, Korea)

  • Dan Keun Sung

    (School of Electrical Engineering, Korea Advanced Institute of Science and Technology, 291 Daehak-ro, Yuseong-gu, Daejeon 34141, Korea)

Abstract

Photovoltaic (PV) output power inherently exhibits an intermittent property depending on the variation of weather conditions. Since PV power producers may be charged to large penalties in forthcoming energy markets due to the uncertainty of PV power generation, they need a more accurate PV power prediction scheme in energy market operation. In this paper, we characterize the effect of PV power prediction errors on energy storage system (ESS)-based PV power trading in energy markets. First, we analyze the prediction accuracy of two machine learning (ML) schemes for the PV output power and estimate their error distributions. We propose an efficient ESS management scheme for charging and discharging operation of ESS in order to reduce the deviations between the day-ahead (DA) and real-time (RT) dispatch in energy markets. In addition, we estimate the capacity of ESSs, which can absorb the prediction errors and then compare the PV power producer’s profit according to ML-based prediction schemes with/without ESS. In case of ML-based prediction schemes with ESS, the ANN and SVM schemes yield a decrease in the deviation penalty by up to 87% and 74%, respectively, compared with the profit of those schemes without ESS.

Suggested Citation

  • Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1249-:d:218969
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    References listed on IDEAS

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    Cited by:

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    2. Jiawei Zhang & Rongquan Zhang & Yanfeng Zhao & Jing Qiu & Siqi Bu & Yuxiang Zhu & Gangqiang Li, 2023. "Deterministic and Probabilistic Prediction of Wind Power Based on a Hybrid Intelligent Model," Energies, MDPI, vol. 16(10), pages 1-15, May.
    3. Junhwa Hwang & Dongjun Suh & Marc-Oliver Otto, 2020. "Forecasting Electricity Consumption in Commercial Buildings Using a Machine Learning Approach," Energies, MDPI, vol. 13(22), pages 1-29, November.
    4. Gang Chen & Qingchang Hu & Jin Wang & Xu Wang & Yuyu Zhu, 2023. "Machine-Learning-Based Electric Power Forecasting," Sustainability, MDPI, vol. 15(14), pages 1-21, July.
    5. Jeong, Jaeik & Kim, Hongseok, 2021. "DeepComp: Deep reinforcement learning based renewable energy error compensable forecasting," Applied Energy, Elsevier, vol. 294(C).
    6. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
    7. Samuel-Soma Ajibade & Abdelhamid Zaidi & Asamh Saleh M. Al Luhayb & Anthonia Oluwatosin Adediran & Liton Chandra Voumik & Fazle Rabbi, 2023. "New Insights into the Emerging Trends Research of Machine and Deep Learning Applications in Energy Storage: A Bibliometric Analysis and Publication Trends," International Journal of Energy Economics and Policy, Econjournals, vol. 13(5), pages 303-314, September.

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