COLLABORATIVE TRAINING STRATEGY OF DISTRIBUTED PV POWER PREDICTION UNDER SCENARIO CLASSIFICATION AND PRIVACY PROTECTION

Deng Fangming; Wu Lei; Wang Jinbo; Wei Baoquan; Gao Bo; Li Zewen

doi:10.19912/j.0254-0096.tynxb.2024-1983

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (7) : 1-10. DOI: 10.19912/j.0254-0096.tynxb.2024-1983

Special Topics of Academic Papers at the 27th Annual Meeting of the China Association for Science and Technology

COLLABORATIVE TRAINING STRATEGY OF DISTRIBUTED PV POWER PREDICTION UNDER SCENARIO CLASSIFICATION AND PRIVACY PROTECTION

Deng Fangming, Wu Lei, Wang Jinbo, Wei Baoquan, Gao Bo, Li Zewen

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Abstract

Due to the spatial dispersion of photovoltaic power stations and the lack of data sharing among users, insufficient power prediction accuracy commonly arises. This paper proposes a collaborative training strategy for distributed PV short-term power prediction based on scenario classification and privacy protection. Firstly, the Pearson correlation coefficient is used to extract important meteorological features, and the fuzzy C-means clustering （fuzzy C-means, FCM） algorithm is used to cluster the historical data set into sunny days, cloudy days and rainy days. Secondly, the regions with similar weather patterns are clustered into several groups, and the sets of sunny and non-sunny under the same category are discriminated and screened to build photovoltaic power prediction models under different scenarios. Then, a multi-task learning algorithm is added on the basis of the general iterative algorithm of federated learning, and a new local training method of multi-task mode is established to preserve the differences among the PV power stations participating in the joint modeling. Finally, the test day is predicted, and the data is input into the prediction model of the corresponding scenario established above, and the photovoltaic power prediction results of the test day are obtained. The experimental findings revealed that, under different weather conditions, compared with various network models, the accuracy of the proposed prediction method is increased by 24.77%, and the root-mean-square error （RMSE） is reduced by 89.24%. Compared with the traditional federated framework, the proposed scheme can achieve the target user identification rate （UA） in faster training rounds, shorten the number of communication rounds by 50% and increase the average UA by 8%. It is verified that this scheme not only improves the accuracy of photovoltaic short-term power prediction, but also has strong adaptability and robustness.

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distributed photovoltaics / power prediction / cluster analysis / multi-task federated learning / scenario classification

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Deng Fangming, Wu Lei, Wang Jinbo, Wei Baoquan, Gao Bo, Li Zewen. COLLABORATIVE TRAINING STRATEGY OF DISTRIBUTED PV POWER PREDICTION UNDER SCENARIO CLASSIFICATION AND PRIVACY PROTECTION[J]. Acta Energiae Solaris Sinica. 2025, 46(7): 1-10 https://doi.org/10.19912/j.0254-0096.tynxb.2024-1983

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