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Title:
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Wind energy forecasting with neural networks: a literature review
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Author:
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Manero, Jaume; Béjar Alonso, Javier; Cortés García, Claudio Ulises
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Ciències de la Computació; Universitat Politècnica de Catalunya. KEMLG - Grup d'Enginyeria del Coneixement i Aprenentatge Automàtic |
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Abstract:
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Renewable energy is intermittent by nature and to integrate this energy into the Grid while assuring safety and stability the accurate forecasting of there newable energy generation is critical. Wind Energy prediction is based on the ability to forecast wind. There are many methods for wind forecasting based on the statistical properties of the wind time series and in the integration of meteorological information, these methods are being used commercially around the world. But one family of new methods for wind power fore castingis surging based on Machine Learning Deep Learning techniques. This paper analyses the characteristics of the Wind Speed time series data and performs a literature review of recently published works of wind power forecasting using Machine Learning approaches (neural and deep learning networks), which have been published in the last few years. |
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Abstract:
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Peer Reviewed |
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Subject(s):
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-Àrees temàtiques de la UPC::Informàtica::Intel·ligència artificial::Aprenentatge automàtic
-Àrees temàtiques de la UPC::Energies::Energia eòlica
-Machine learning
-Neural networks (Computer science)
-Forecasting
-Wind power
-Wind power forecast
-Wind speed forecast
-Short-term prediction
-Deep learning
-Literature review
-Aprenentatge automàtic
-Xarxes neuronals (Informàtica)
-Previsió
-Energia eòlica |
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Rights:
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Document type:
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Article - Published version
Article |
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