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Ultra-Short-Term Photovoltaic Power Prediction Model Based on the Localized Emotion Reconstruction Emotional Neural Network

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  • Yufei Wang

    (College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Li Zhu

    (College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

  • Hua Xue

    (College of Electrical Engineering, Shanghai University of Electric Power, Shanghai 200090, China)

Abstract

Due to the intermittency and randomness of photovoltaic (PV) power, the PV power prediction accuracy of the traditional data-driven prediction models is difficult to improve. A prediction model based on the localized emotion reconstruction emotional neural network (LERENN) is proposed, which is motivated by chaos theory and the neuropsychological theory of emotion. Firstly, the chaotic nonlinear dynamics approach is used to draw the hidden characteristics of PV power time series, and the single-step cyclic rolling localized prediction mechanism is derived. Secondly, in order to establish the correlation between the prediction model and the specific characteristics of PV power time series, the extended signal and emotional parameters are reconstructed with a relatively certain local basis. Finally, the proposed prediction model is trained and tested for single-step and three-step prediction using the actual measured data. Compared with the prediction model based on the long short-term memory (LSTM) neural network, limbic-based artificial emotional neural network (LiAENN), the back propagation neural network (BPNN), and the persistence model (PM), numerical results show that the proposed prediction model achieves better accuracy and better detection of ramp events for different weather conditions when only using PV power data.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:11:p:2857-:d:367016
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    References listed on IDEAS

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    1. 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.
    2. 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.
    3. Honglu Zhu & Xu Li & Qiao Sun & Ling Nie & Jianxi Yao & Gang Zhao, 2015. "A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks," Energies, MDPI, vol. 9(1), pages 1-15, December.
    4. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
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    Cited by:

    1. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.
    2. Chen, Xiang & Ding, Kun & Zhang, Jingwei & Han, Wei & Liu, Yongjie & Yang, Zenan & Weng, Shuai, 2022. "Online prediction of ultra-short-term photovoltaic power using chaotic characteristic analysis, improved PSO and KELM," Energy, Elsevier, vol. 248(C).

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