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• W4378902009 abstract "Abstract Photovoltaic (PV) is essential for global carbon neutrality, it is imperative to forecast PV generation accurately for power operation. With the rapid growth of distributed PV sites, a scalable cloud service tends to play a vital role in PV forecasting to address the increasing cost of computing resources and data subscriptions. Such a scheme creates a possibility to further enhance forecasting performance by re‐using forecasting model, data, and computing resources all in the cloud. In order to achieve this goal, this work proposes a multi‐site PV forecasting system design with a message queue (MQ) and stream computing engine, where a hybrid neural network model is trained and continuously updated using real‐time data. A performance benchmark with up to 60 sites served simultaneously was performed to verify the scalability of the stream computing based approach. Moreover, after incremental updating of the forecasting model, a decrease in normalized root mean square error and normalized mean absolute error of PV forecasting were observed, demonstrating that better short‐term forecasting accuracy was achieved." @default.
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• W4378902009 date "2023-05-30" @default.
• W4378902009 modified "2023-10-02" @default.
• W4378902009 title "Scalable multi‐site photovoltaic power forecasting based on stream computing" @default.
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• W4378902009 doi "https://doi.org/10.1049/rpg2.12766" @default.
• W4378902009 hasPublicationYear "2023" @default.
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