A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

•A novel deep learning-based evolutionary model proposed for wind speed forecasting.•A new evolutionary hierarchy-based decomposition method introduced.•A developed evolutionary algorithm proposed in order to hyper-parameter tuning.•Generalised normal distribution algorithm is improved by an adaptiv...

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Veröffentlicht in:	Energy conversion and management Jg. 236; S. 114002
Hauptverfasser:	Neshat, Mehdi, Nezhad, Meysam Majidi, Abbasnejad, Ehsan, Mirjalili, Seyedali, Tjernberg, Lina Bertling, Astiaso Garcia, Davide, Alexander, Bradley, Wagner, Markus
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Oxford Elsevier Ltd 15.05.2021 Elsevier Science Ltd
Schlagworte:	administrative management Algorithms Baltic Sea case studies Deep learning Deep learning models Electric power generation energy conversion Energy demand Evolution Evolutionary algorithms Forecasting Generalised normal distribution optimisation Hybrid evolutionary deep learning method Long short-term memory Machine learning Mathematical models Neural networks Normal distribution Offshore energy sources Optimization power generation prediction Short-term forecasting Turbines Wind farms Wind power Wind power generation Wind speed Wind speed prediction Wind turbines Sweden Baltic Sea Deep learning models Short-term forecasting Wind speed prediction Evolutionary algorithms Generalised normal distribution optimisation Hybrid evolutionary deep learning method
ISSN:	0196-8904, 1879-2227, 1879-2227
Online-Zugang:	Volltext
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Zusammenfassung:	•A novel deep learning-based evolutionary model proposed for wind speed forecasting.•A new evolutionary hierarchy-based decomposition method introduced.•A developed evolutionary algorithm proposed in order to hyper-parameter tuning.•Generalised normal distribution algorithm is improved by an adaptive local search.•The proposed hybrid model improves the accuracy of short-term wind speed. Due to expanding global environmental issues and growing energy demand, wind power technologies have been studied extensively. Accurate and robust short-term wind speed forecasting is crucial for large-scale integration of wind power generation into the power grid. However, the seasonal and stochastic characteristics of wind speed make forecasting a challenging task. This study adopts a novel hybrid deep learning-based evolutionary approach in an attempt to improve the accuracy of wind speed prediction. This hybrid model consists of a bidirectional long short-term memory neural network, an effective hierarchical evolutionary decomposition technique and an improved generalised normal distribution optimisation algorithm for hyper-parameter tuning. The proposed hybrid approach was trained and tested on data gathered from an offshore wind turbine installed in a Swedish wind farm located in the Baltic Sea with two forecasting horizons: ten-minutes ahead and one-hour ahead. The experimental results indicated that the new approach is superior to six other applied machine learning models and a further seven hybrid models, as measured by seven performance criteria.
Bibliographie:	ObjectType-Case Study-2 SourceType-Scholarly Journals-1 content type line 14 ObjectType-Feature-4 ObjectType-Report-1 ObjectType-Article-3 ObjectType-Article-1 ObjectType-Feature-2 content type line 23
ISSN:	0196-8904 1879-2227 1879-2227
DOI:	10.1016/j.enconman.2021.114002