DISTRIBUTED PHOTOVOLTAIC SHORT-TERM POWER PREDICITION METHOD BASED ON HORIZONTAL FEDERAL LEARNING

Meng Fei; Li Jiangpeng; Wang Xin; Xu Hengshan; Qi Xin; Yang Huibiao

doi:10.19912/j.0254-0096.tynxb.2023-1553

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Acta Energiae Solaris Sinica ›› 2025, Vol. 46 ›› Issue (1) : 686-695. DOI: 10.19912/j.0254-0096.tynxb.2023-1553

DISTRIBUTED PHOTOVOLTAIC SHORT-TERM POWER PREDICITION METHOD BASED ON HORIZONTAL FEDERAL LEARNING

Qi Xin¹, Yang Huibiao², Meng Fei¹, Li Jiangpeng¹, Wang Xin¹, Xu Hengshan³

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Abstract

Aiming at the problem that most distributed PV systems are unable to cope with the impact of the complex energy-use behaviour of the users they carry on the grid-connection process, and that the privacy of user data leads to the inapplicability of traditional centralized prediction algorithms, an ultra-short-term distributed PV power prediction method based on transversal federated learning is proposed, which combines the information on historical data, PV array's output and users' energy-use behaviour to construct an AdaRNN-based power prediction model at the local site. The power prediction model based on AdaRNN is constructed at the local site, and the feature parameters are extracted using gated loop units, which are used for the aggregation and optimisation of the model parameters by the horizontal federated learning network to achieve the availability of the user data without visibility. Simulation validation shows that the method achieves the protection of user privacy data and effectively improves the accuracy of distributed PV ultra-short-term prediction power.

Key words

distributed photovoltaic / ultra-short-term power prediction / user privacy / transversal federation learning / deep learning

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Qi Xin, Yang Huibiao, Meng Fei, Li Jiangpeng, Wang Xin, Xu Hengshan. DISTRIBUTED PHOTOVOLTAIC SHORT-TERM POWER PREDICITION METHOD BASED ON HORIZONTAL FEDERAL LEARNING[J]. Acta Energiae Solaris Sinica. 2025, 46(1): 686-695 https://doi.org/10.19912/j.0254-0096.tynxb.2023-1553

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