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- W2775303493 abstract "The greatest concern facing renewable energy sources like wind is the uncertainty in production volumes as their generation ability is inherently dependent on weather conditions. When providing forecasts for newly commissioned wind farms there is a limited amount of historical power production data, while the number of potential features from different weather forecast providers is vast. Bayesian regularization is therefore seen as a possible technique for reducing model overfitting problems that may arise. This work investigates Bayesian Neural Networks for one-hour ahead forecasting of wind power generation. Initial results show that Bayesian Neural Networks display equivalent predictive performance to Neural Networks trained by Maximum Likelihood. Further results show that Bayesian Neural Networks become superior after removing irrelevant features using Automatic Relevance Determination(ARD)." @default.
- W2775303493 created "2017-12-22" @default.
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- W2775303493 date "2017-11-01" @default.
- W2775303493 modified "2023-10-18" @default.
- W2775303493 title "Bayesian neural networks for one-hour ahead wind power forecasting" @default.
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- W2775303493 doi "https://doi.org/10.1109/icrera.2017.8191129" @default.
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