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Content Provider | IET Digital Library |
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Author | Zhang, Chi Li, Ran Shi, Heng Li, Furong |
Abstract | 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 a deregulated electricity market to explore the complex dependence structure of the multivariate EPF model. The proposed method can learn the indirect relationship between electricity price and external factors through its efficient diverse function and multi-layer structure. The effectiveness of the method is validated using data from the New England electricity market. Compared with the up-to-date techniques, the proposed DRNN outperforms the single support vector machine (SVM) by 29.71%, and the improved hybrid SVM network by 21.04% in terms of mean absolute percentage error. |
Starting Page | 462 |
Ending Page | 469 |
Page Count | 8 |
Volume Number | 3 |
e-ISSN | 25152947 |
Issue Number | Issue 4, Aug (2020) |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/iet-stg/3/4 |
Alternate Webpage(s) | https://digital-library.theiet.org/content/journals/10.1049/iet-stg.2019.0258 |
Journal | IET Smart Grid |
Publisher | The Institution of Engineering and Technology |
Publisher Date | 2020-02-25 |
Access Restriction | Open |
Rights License | Creative Commons Attribution-Non Commercial-No Derivs License (http://creativecommons.org/licenses/by-nc-nd/3.0/) |
Subject Keyword | Accurate Electricity Price Forecasting Day-ahead Electricity Price Forecasting Deep Learning Deep Recurrent Neural Network Method Deregulated Electricity Market Economic Forecasting Electricity Consumption Forecasting Model Knowledge Engineering Technique Learning in AI Market Participants Multivariate EPF Model Natural Gas Price Neural Computing Technique New England Electricity Market Power Engineering Computing Power Market Power System Economics Power System Managemen Power System Operation Price Movement Pricing Recurrent Neural Nets Statistics Support Vector Machine |
Content Type | Text |
Resource Type | Article |
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