Deep learning for day-ahead electricity price forecasting

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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Bibliographic Details
Main Authors: Chi Zhang, Ran Li, Heng Shi, Furong Li
Format: Article
Language:English
Published: Wiley 2020-02-01
Series:IET Smart Grid
Subjects:
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
Online Access:https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0258

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https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0258

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