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دسته بندی:
یادگیری عمیق - deep learning
سال انتشار:
2019
عنوان انگلیسی مقاله:
Bayesian deep learning based method for probabilistic forecast of day-ahead electricity prices
ترجمه فارسی عنوان مقاله:
روش مبتنی بر یادگیری عمیق بیزی برای پیش بینی احتمالی قیمت برق روز پیش رو
منبع:
Sciencedirect - Elsevier - Applied Energy, 250 (2019) 1158-1175: doi:10:1016/j:apenergy:2019:05:068
نویسنده:
Alessandro Brusaferria,b,⁎, Matteo Matteuccib, Pietro Portolania, Andrea Vitalia
چکیده انگلیسی:
The availability of accurate day-ahead energy prices forecasts is crucial to achieve a successful participation to
liberalized electricity markets. Moreover, forecasting systems providing prediction intervals and densities (i.e.
probabilistic forecasting) are fundamental to enable enhanced bidding and planning strategies considering
uncertainty explicitly. Nonetheless, the vast majority of available approaches focus on point forecast. Therefore,
we propose a novel methodology for probabilistic energy price forecast based on Bayesian deep learning
techniques. A specific training method has been deployed to guarantee scalability to complex network architectures.
Moreover, we developed a model originally supporting heteroscedasticity, thus avoiding the common
homoscedastic assumption with related preprocessing effort. Experiments have been performed on two dayahead
markets characterized by different behaviors. Then, we demonstrated the capability of the proposed
method to achieve robust performances in out-of-sample conditions while providing forecast uncertainty indications..
Keywords: Electricity price forecasting | Probabilistic forecasting | Deep learning | Bayesian learning | Neural network
قیمت: رایگان
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