Abstract
Forecasting electricity prices accurately is an essential requirement for the players in the market. The sequential data of electricity poses many challenges such as seasonality and high volatility. Recently, recurrent neural network based methods have showed great success in forecasting time series type problems. In this paper, we propose to use an encoder-decoder recurrent neural network for forecasting hourly electricity prices in the Turkish Day-ahead Market. The approach involves two recurrent neural networks, one to encode the source sequence, called the encoder, and a second to decode the encoded source sequence into the target sequence, called the decoder. We trained and tested our framework on the Turkish electricity price data from 2013 to 2016 and report the accuracy of various recurrent neural network models in terms of mean absolute error. The encoder-decoder recurrent neural networks achieved better accuracy compared to classical recurrent neural networks.
Translated title of the contribution | Electricity Price Prediction Using Encoder-Decoder Recurrent Neural Networks in Turkish Dayahead Market |
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Original language | Turkish |
Title of host publication | 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings |
Publisher | Institute of Electrical and Electronics Engineers Inc. |
ISBN (Electronic) | 9781728172064 |
DOIs | |
Publication status | Published - 5 Oct 2020 |
Event | 28th Signal Processing and Communications Applications Conference, SIU 2020 - Gaziantep, Turkey Duration: 5 Oct 2020 → 7 Oct 2020 |
Publication series
Name | 2020 28th Signal Processing and Communications Applications Conference, SIU 2020 - Proceedings |
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Conference
Conference | 28th Signal Processing and Communications Applications Conference, SIU 2020 |
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Country/Territory | Turkey |
City | Gaziantep |
Period | 5/10/20 → 7/10/20 |
Bibliographical note
Publisher Copyright:© 2020 IEEE.