One-Day-Ahead Solar Irradiation and Windspeed Forecasting with Advanced Deep Learning Techniques

Konstantinos Blazakis, Yiannis Katsigiannis and Georgios Stavrakakis
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Konstantinos Blazakis: School of Electrical and Computer Engineering, Technical University of Crete, GR-73100 Chania, Greece
Yiannis Katsigiannis: Department of Electrical and Computer Engineering, Hellenic Mediterranean University, GR-71004 Heraklion, Greece
Georgios Stavrakakis: School of Electrical and Computer Engineering, Technical University of Crete, GR-73100 Chania, Greece

Energies, 2022, vol. 15, issue 12, 1-25

Abstract: In 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.

Keywords: artificial intelligence; data mining; machine learning; advanced deep learning; windspeed forecasting; solar irradiation forecasting; increased RES penetration (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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