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Short-Term Photovoltaic Power Output Prediction Based on k -Fold Cross-Validation and an Ensemble Model

Author

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  • Ruijin Zhu

    (Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, Tibet Autonomous Region, China)

  • Weilin Guo

    (Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, Tibet Autonomous Region, China)

  • Xuejiao Gong

    (Electric Engineering College, Tibet Agriculture and Animal Husbandry University, Nyingchi 860000, Tibet Autonomous Region, China)

Abstract

Short-term photovoltaic power forecasting is of great significance for improving the operation of power systems and increasing the penetration of photovoltaic power. To improve the accuracy of short-term photovoltaic power forecasting, an ensemble-model-based short-term photovoltaic power prediction method is proposed. Firstly, the quartile method is used to process raw data, and the Pearson coefficient method is utilized to assess multiple features affecting the short-term photovoltaic power output. Secondly, the structure of the ensemble model is constructed, and a k -fold cross-validation method is used to train the submodels. The prediction results of each submodel are merged. Finally, the validity of the proposed approach is verified using an actual data set from State Power Investment Corporation Limited. The simulation results show that the quartile method can find outliers which contributes to processing the raw data and improving the accuracy of the model. The k -fold cross-validation method can effectively improve the generalization ability of the model, and the ensemble model can achieve higher prediction accuracy than a single model.

Suggested Citation

  • Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Photovoltaic Power Output Prediction Based on k -Fold Cross-Validation and an Ensemble Model," Energies, MDPI, vol. 12(7), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:7:p:1220-:d:218211
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    Cited by:

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    3. Tito G. Amaral & Vitor Fernão Pires & Armando J. Pires, 2021. "Fault Detection in PV Tracking Systems Using an Image Processing Algorithm Based on PCA," Energies, MDPI, vol. 14(21), pages 1-18, November.
    4. Ivan Lorencin & Nikola Anđelić & Vedran Mrzljak & Zlatan Car, 2019. "Genetic Algorithm Approach to Design of Multi-Layer Perceptron for Combined Cycle Power Plant Electrical Power Output Estimation," Energies, MDPI, vol. 12(22), pages 1-26, November.
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    6. Yufei Wang & Li Zhu & Hua Xue, 2020. "Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network," Energies, MDPI, vol. 13(11), pages 1-21, June.
    7. Ruijin Zhu & Bo Tang & Wenhai Wei, 2022. "Ensemble Learning-Based Reactive Power Optimization for Distribution Networks," Energies, MDPI, vol. 15(6), pages 1-15, March.
    8. Rai, Amit & Shrivastava, Ashish & Jana, Kartick C., 2023. "Differential attention net: Multi-directed differential attention based hybrid deep learning model for solar power forecasting," Energy, Elsevier, vol. 263(PC).
    9. Sarmas, Elissaios & Spiliotis, Evangelos & Stamatopoulos, Efstathios & Marinakis, Vangelis & Doukas, Haris, 2023. "Short-term photovoltaic power forecasting using meta-learning and numerical weather prediction independent Long Short-Term Memory models," Renewable Energy, Elsevier, vol. 216(C).

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