Scalable multi‐site photovoltaic power forecasting based on stream computing

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.

Keywords

• computer centres
• data mining
• neural nets
• stream incremental learning
• solar photovoltaic systems