Automated Adaptive-Ensemble Framework for Large Wind Power Prediction in Poland using Deep Learning Models
Artykuł w czasopiśmie
MNiSW
100
Lista 2021
| Status: | |
| Autorzy: | Wydra Michał, Kozłowski Mateusz, Czerwiński Dariusz, Księżopolski Bogdan |
| Dyscypliny: | |
| Aby zobaczyć szczegóły należy się zalogować. | |
| Rok wydania: | 2022 |
| Wersja dokumentu: | Drukowana | Elektroniczna |
| Język: | angielski |
| Numer czasopisma: | 6 |
| Wolumen/Tom: | 16 |
| Strony: | 214 - 225 |
| Web of Science® Times Cited: | 1 |
| Scopus® Cytowania: | 1 |
| Bazy: | Web of Science | Scopus | Baztech |
| Efekt badań statutowych | NIE |
| Materiał konferencyjny: | NIE |
| Publikacja OA: | TAK |
| Licencja: | |
| Sposób udostępnienia: | Otwarte czasopismo |
| Wersja tekstu: | Ostateczna wersja opublikowana |
| Czas opublikowania: | W momencie opublikowania |
| Data opublikowania w OA: | 1 grudnia 2022 |
| Abstrakty: | angielski |
| Prediction of considerable wind power is a significant factor in modern power systems’ robust and resilient operation. As a result, many studies addressed up-to-day-ahead wind power forecasting. Taking into account the abilities of Machine Learning (ML) Models and their combinations, in this paper, the Authors would like to present the framework for 60-hour wind power forecasting in Poland. Possession of longer than one-day ahead of accurate wind power forecasts gives an ability to the Transmission System Operator (TSO) and Distribution System Operator (DSO) the to improve the control of grid traffic and assimilate more efficiently the Renewable Energy Sources (RES) generated power. The presented method uses the geographical coordinates of wind farms in the area of interest with their wind-power curves. It combines it with weather parameters such as wind speed and direction, wind gust, air temperature, and pressure for improved accuracy. The novelty of the proposed method is that model can adapt autonomously according to achieved past accuracy. A back-score accuracy has been tracked based on past predictions and measured wind power generation. Furthermore, the model combines different ML models that can be adapted or retrained if prediction performance drops below an acceptable level. In this paper, we would like to present a method and performance analysis of the adaptive ensemble-model method, whose accuracy has been calculated compared with actual data measured in the National Power System of Poland. Furthermore, the paper’s novelty is that the proposed method/framework can use only an approximate model of large wind generation at the beginning, and the model can be fine-tuned in the proposed method’s operation as a function of back-accuracy measurement triggering ensemble-model adaptation. |
