Statistical Method for Photovoltaic Power Forecasting Basing on Signal Components Decomposition
Ładowanie...
Data
2023
Autorzy
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
Since 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.
Opis
Słowa kluczowe
RES, time series forecasting, PV production forecasting, AR, renewable energy sources, photovoltaic, auto regressive model, OZE, prognozowanie szeregów czasowych, prognozowanie produkcji PV, AR, odnawialne źródła energii, fotowoltaika, model autoregresyjny
Cytowanie
Parczyk 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.