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One-day-ahead solar irradiation and windspeed forecasting with advanced deep learning techniques

Blazakis Konstantinos, Katsigiannis Ioannis, Stavrakakis Georgios

Απλή Εγγραφή


URIhttp://purl.tuc.gr/dl/dias/B1E0EC82-40AF-4DD1-A4D5-D9D5E268517A-
Αναγνωριστικόhttps://doi.org/10.3390/en15124361-
Αναγνωριστικόhttps://www.mdpi.com/1996-1073/15/12/4361-
Γλώσσαen-
Μέγεθος25 pagesen
ΤίτλοςOne-day-ahead solar irradiation and windspeed forecasting with advanced deep learning techniquesen
ΔημιουργόςBlazakis Konstantinosen
ΔημιουργόςΜπλαζακης Κωνσταντινοςel
ΔημιουργόςKatsigiannis Ioannisen
ΔημιουργόςΚατσιγιαννης Ιωαννηςel
ΔημιουργόςStavrakakis Georgiosen
ΔημιουργόςΣταυρακακης Γεωργιοςel
ΕκδότηςMDPIen
ΠερίληψηIn recent years, demand for electric energy has steadily increased; therefore, the integration of renewable energy sources (RES) at a large scale into power systems is a major concern. Wind and solar energy are among the most widely used alternative sources of energy. However, there is intense variability both in solar irradiation and even more in windspeed, which causes solar and wind power generation to fluctuate highly. As a result, the penetration of RES technologies into electricity networks is a difficult task. Therefore, more accurate solar irradiation and windspeed one-day-ahead forecasting is crucial for safe and reliable operation of electrical systems, the management of RES power plants, and the supply of high-quality electric power at the lowest possible cost. Clouds’ influence on solar irradiation forecasting, data categorization per month for successive years due to the similarity of patterns of solar irradiation per month during the year, and relative seasonal similarity of windspeed patterns have not been taken into consideration in previous work. In this study, three deep learning techniques, i.e., multi-head CNN, multi-channel CNN, and encoder–decoder LSTM, were adopted for medium-term windspeed and solar irradiance forecasting based on a real-time measurement dataset and were compared with two well-known conventional methods, i.e., RegARMA and NARX. Utilization of a walk-forward validation forecast strategy was combined, firstly with a recursive multistep forecast strategy and secondly with a multiple-output forecast strategy, using a specific cloud index introduced for the first time. Moreover, the similarity of patterns of solar irradiation per month during the year and the relative seasonal similarity of windspeed patterns in a timeseries measurements dataset for several successive years demonstrates that they contribute to very high one-day-ahead windspeed and solar irradiation forecasting performance.en
ΤύποςPeer-Reviewed Journal Publicationen
ΤύποςΔημοσίευση σε Περιοδικό με Κριτέςel
Άδεια Χρήσηςhttp://creativecommons.org/licenses/by/4.0/en
Ημερομηνία2023-08-29-
Ημερομηνία Δημοσίευσης2022-
Θεματική ΚατηγορίαArtificial intelligenceen
Θεματική ΚατηγορίαData miningen
Θεματική ΚατηγορίαMachine learningen
Θεματική ΚατηγορίαAdvanced deep learningen
Θεματική ΚατηγορίαWindspeed forecastingen
Θεματική ΚατηγορίαSolar irradiation forecastingen
Θεματική ΚατηγορίαIncreased RES penetrationen
Βιβλιογραφική ΑναφοράK. Blazakis, Y. Katsigiannis, and G. Stavrakakis, “One-day-ahead solar irradiation and windspeed forecasting with advanced deep learning techniques,” Energies, vol. 15, no. 12, June 2022, doi: 10.3390/en15124361.en

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