Sarkis, RoyOguz, IlkerPsaltis, DemetriPaolone, MarioMoser, ChristopheLambertini, Luisa2024-07-032024-07-032024-07-032024-06-0110.1016/j.solener.2024.112600https://infoscience.epfl.ch/handle/20.500.14299/209086WOS:001248361800001With the significant increase in photovoltaic (PV) electricity generation, more attention has been given to PV power forecasting. Indeed, accurate forecasting allows power grid operators to better schedule and dispatch their assets, such as energy storage systems and reserve. In this paper, a hybrid deep learning model and a convolutional neural network with memory is proposed, to provide intraday (2 h) solar irradiance forecasts using sequentially -collected images from public webcams. The performance of the proposed model is compared to those of a standard time -series forecast models, a linear regression as well as state-of-the-art neural networks. All models are trained and tested using images collected from two webcams on EPFL's campus for just over a year. The results show that the proposed method outperforms all other models and matches the state-of-the-art methodology while providing simplicity of implementation and efficient computation.TechnologyPublic WebcamsMachine LearningDeep Neural NetworkCnnLstmGhitext::journal::journal article::research article