Machine learning methods for solar radiation forecasting: A review
Cyril Voyant,Gilles Notton,Soteris A. Kalogirou,Marie Laure Nivet,Christophe Paoli,Christophe Paoli,Fabrice Motte,Alexis Fouilloy +7 more
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TLDR
An overview of forecasting methods of solar irradiation using machine learning approaches is given and it will be shown that other methods begin to be used in this context of prediction.About:
This article is published in Renewable Energy.The article was published on 2017-05-01 and is currently open access. It has received 1095 citations till now. The article focuses on the topics: Probabilistic forecasting & Solar irradiance.read more
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Statistical and Machine Learning forecasting methods: Concerns and ways forward.
TL;DR: It is found that the post-sample accuracy of popular ML methods are dominated across both accuracy measures used and for all forecasting horizons examined, and that their computational requirements are considerably greater than those of statistical methods.
Online Short-term Solar Power Forecasting
TL;DR: The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 h, where the results indicate that for forecasts up to 2 h ahead the most important input is the available observations ofSolar power, while for longer horizons NWPs are theMost important input.
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Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM
Xiangyun Qing,Yugang Niu +1 more
TL;DR: A novel solar prediction scheme for hourly day-ahead solar irradiance prediction by using the weather forecasting data is proposed and it is demonstrated that the proposed algorithm outperforms these competitive algorithms for single output prediction.
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A review and evaluation of the state-of-the-art in PV solar power forecasting:Techniques and optimization
TL;DR: In this paper, the authors reviewed and evaluated contemporary forecasting techniques for photovoltaics into power grids, and concluded that ensembles of artificial neural networks are best for forecasting short-term PV power forecast and online sequential extreme learning machine superb for adaptive networks; while Bootstrap technique optimum for estimating uncertainty.
Posted Content
Tackling Climate Change with Machine Learning
David Rolnick,Priya L. Donti,Lynn H. Kaack,K. Kochanski,Alexandre Lacoste,Kris Sankaran,Andrew S. Ross,Nikola Milojevic-Dupont,Natasha Jaques,Anna Waldman-Brown,Alexandra Luccioni,Tegan Maharaj,Evan D. Sherwin,S. Karthik Mukkavilli,Konrad P. Kording,Carla P. Gomes,Andrew Y. Ng,Demis Hassabis,John Platt,Felix Creutzig,Jennifer Chayes,Yoshua Bengio +21 more
TL;DR: From smart grids to disaster management, high impact problems where existing gaps can be filled by ML are identified, in collaboration with other fields, to join the global effort against climate change.
References
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Ensemble Methods in Machine Learning
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