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Deep Learning for Day-Ahead Electricity Price Forecasting
2020
IET Smart Grid
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 into the forecasting model, such as weather, electricity consumption and natural gas price. This study proposes a deep recurrent neural network (DRNN) method to forecast day-ahead electricity price in
doi:10.1049/iet-stg.2019.0258
fatcat:57nogjfq65fhbdgnpanpzgfeku