Demystifying the use of ERA5‐land and machine learning for wind power forecasting

Alexander Marinšek, Gregor Bajt

IET Renewable Power Generation 2020 article cited by 20

Source More in Climate & Environment

importance 5/10 ai assisted Energy Load and Power Forecasting renewable energy stage: research

Problem

Short-term wind power forecasting to mitigate wind inconsistency effects on the electric grid.

Accurate forecasting reduces need for costly energy storage and stabilizes renewable grid integration.

Random forest, support vector regression, and LSTM models were tuned and tested for wind power forecasting using the ERA5-Land dataset.

Both ML and DL models performed better than the baseline model for wind speed forecasting up to 24 hours ahead.

Showed feasibility of free, open-data-based ML forecasting outperforming a baseline.

Venue	IET Renewable Power Generation
Published	2020
Work type	article
Authors	Alexander Marinšek, Gregor Bajt
Citations	20 (OpenAlex, at import time)
References	44
Model or method	Random Forest, SVR, LSTM
Organization	not stated in source
Primary source	https://doi.org/10.1049/iet-rpg.2020.0576
Open access	closed
Keywords	wind power, forecasting, machine learning, LSTM, ERA5-Land, renewable energy

5 / 10

real but narrower advance

Wind forecasting aids grid stability and avoids costly storage investment; feasibility study with free open data, benefit not quantified at scale.

Basis: inferred. Judged from the source text, which gives no explicit scale. Stated scale: not stated in source. Method and limits.

#	Paper	Year	Citations	Importance
1	Learning skillful medium-range global weather forecasting Rémi Lam, Álvaro Sánchez‐González, Matthew Willson, Peter Wirnsberger,...	2023	1,416	8
2	Accurate medium-range global weather forecasting with 3D neural networks Kaifeng Bi, Lingxi Xie, Hengheng Zhang, Xin Chen, Xiaotao Gu, Qi Chuan...	2023	1,631	8
3	FuXi: a cascade machine learning forecasting system for 15-day global weather forecast Lei Chen, Xiaohui Zhong, Feng Zhang, Yuan Cheng, Yinghui Xu, Qi Yuan,...	2023	355	8
4	Skilful nowcasting of extreme precipitation with NowcastNet Yuchen Zhang, Mingsheng Long, Kaiyuan Chen, Lanxiang Xing, Ronghua Jin...	2023	451	8
5	Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects Natei Ermias Benti, Mesfin Diro Chaka, Addisu Gezahegn Semie	2023	378	8