Comparative Study of Machine Learning Techniques to Forecast Short-Term Wind Power
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Publisher
Springer Nature Singapore
Link
https://link.springer.com/content/pdf/10.1007/978-981-19-3311-0_46
Reference33 articles.
1. Habib A, Abbassi R, Aristizábal AJ, Abbassi A (2020) Forecasting model for wind power integrating least squares support vector machine, singular spectrum analysis, deep belief network, and locality-sensitive hashing. Wind Energy 23(2):235–257
2. Jørgensen KL, Shaker HR (2020) Wind power forecasting using machine learning: state of the art, trends and challenges. In: 2020 IEEE 8th International conference on smart energy grid engineering (SEGE). IEEE, pp 44–50
3. Wang S, Li B, Li G, Yao B, Wu J (2021) Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration. Appl Energy 292:116851
4. Lu P, Ye L, Zhao Y, Dai B, Pei M, Tang Y (2021) Review of meta-heuristic algorithms for wind power prediction: methodologies, applications and challenges. Appl Energy 301:117446
5. Wang L, Tao R, Hu H, Zeng YR (2021) Effective wind power prediction using novel deep learning network: stacked independently recurrent autoencoder. Renew Energy 164:642–655
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