Parczyk, PawełBurduk, Robert2023-09-222023-09-222023Parczyk P., Burduk R., Statistical Method for Photovoltaic Power Forecasting Basing on Signal Components Decomposition. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 207-212, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.33.978-83-66741-92-8http://hdl.handle.net/11652/4809https://doi.org/10.34658/9788366741928.33Since climate and environmental protection have become an important point for society, the industry and business have focused on increasing the share of renewable energy sources in the energy mix. This brought us new challenges. In this paper, we propose a method for photovoltaic power production forecasting. We compared our model with a state-of-the-art Auto Regressive model. We used Mean Absolute Error and Mean Absolute Percentage Error as metrics. Finally, our model turned out to be statistically better than reference model in generating one-hour and two-and-a-half-hour forecasts.enDla wszystkich w zakresie dozwolonego użytkuFair use conditionREStime series forecastingPV production forecastingARrenewable energy sourcesphotovoltaicauto regressive modelOZEprognozowanie szeregów czasowychprognozowanie produkcji PVARodnawialne źródła energiifotowoltaikamodel autoregresyjnyRozdział - monografiaLicencja PŁLUT License10.34658/9788366741928.33