Deep learning for day-ahead electricity price forecasting

Deregulation exposes the inherent volatility of the electricity price. Accurate electricity price forecasting (EPF) could help the market participants to hedge against the price movements and maximise their profits. The existing methods have limited capability of integrating other external factors i...

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محفوظ في:

التفاصيل البيبلوغرافية
المؤلفون الرئيسيون:	Chi Zhang, Ran Li, Heng Shi, Furong Li
التنسيق:	Artigo
اللغة:	Inglês
منشور في:	Wiley 2020-02-01
سلاسل:	IET Smart Grid
الموضوعات:	learning (artificial intelligence) power system economics power engineering computing economic forecasting pricing support vector machines recurrent neural nets power markets deregulated electricity market multivariate epf model new england electricity market deep learning day-ahead electricity price forecasting accurate electricity price forecasting market participants price movements forecasting model electricity consumption natural gas price deep recurrent neural network method
الوصول للمادة أونلاين:	https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0258
الوسوم:	إضافة وسم لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!

Deep learning for day-ahead electricity price forecasting

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