Institutional Repository [SANDBOX]
Technical University of Crete
EN  |  EL

Search

Browse

My Space

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

Blazakis Konstantinos, Katsigiannis Ioannis, Stavrakakis Georgios

Simple record


URIhttp://purl.tuc.gr/dl/dias/B1E0EC82-40AF-4DD1-A4D5-D9D5E268517A-
Identifierhttps://doi.org/10.3390/en15124361-
Identifierhttps://www.mdpi.com/1996-1073/15/12/4361-
Languageen-
Extent25 pagesen
TitleOne-day-ahead solar irradiation and windspeed forecasting with advanced deep learning techniquesen
CreatorBlazakis Konstantinosen
CreatorΜπλαζακης Κωνσταντινοςel
CreatorKatsigiannis Ioannisen
CreatorΚατσιγιαννης Ιωαννηςel
CreatorStavrakakis Georgiosen
CreatorΣταυρακακης Γεωργιοςel
PublisherMDPIen
Content SummaryIn 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
Type of ItemPeer-Reviewed Journal Publicationen
Type of ItemΔημοσίευση σε Περιοδικό με Κριτέςel
Licensehttp://creativecommons.org/licenses/by/4.0/en
Date of Item2023-08-29-
Date of Publication2022-
SubjectArtificial intelligenceen
SubjectData miningen
SubjectMachine learningen
SubjectAdvanced deep learningen
SubjectWindspeed forecastingen
SubjectSolar irradiation forecastingen
SubjectIncreased RES penetrationen
Bibliographic CitationK. 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

Available Files

Services

Statistics