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Optimized Gated Recurrent Unit for Mid-Term Electricity Price Forecasting

Computer Systems Science & Engineering 2022年第11期43卷 817-832页

作者： Rashed Iqbal Hazlie Mokhlis Anis Salwa Mohd Khairuddin syafiqah ismail Munir Azam Muhammad Department of Electrical Engineering Faculty of EngineeringUniversiti Malaya50603Malaysia Department of Electrical Engineering Main CampusIqra UniversityKarachi75300Pakistan

Electricity price forecasting(EPF)is important for energy system operations and management which include strategic bidding,generation scheduling,optimum storage reserves scheduling and systems ***,accurate EPF is crucial for the purpose of bidding strategies and minimizing the risk for market participants in the competitive electricity ***,accurate time-series prediction of electricity price is very challenging due to complex nonlinearity in the trend of electricity *** work proposes a mid-term forecasting model based on the demand and price data,renewable and non-renewable energy supplies,the seasonality and peak and off-peak hours of working and nonworking *** optimized Gated Recurrent Unit(GRU)which incorporates Bagged Regression Tree(BTE)is developed in the Recurrent Neural Network(RNN)architecture for the mid-term *** layer is employed to optimize the hyperparameters of the heterogeneous GRU with the aim to improve the model’s performance,error reduction and predict the *** this work,the proposed framework is assessed using electricity market data of five major economical states in Australia by using electricity market data from August 2020 to May *** results showed significant improvement when adopting the proposed prediction framework compared to previous works in forecasting the electricity price.

关键词： Deep learning energy management machine learning prediction

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