Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods
In this paper day-ahead electricity price forecasting for the Denmark-West region is realized with a 24 h forecasting range. The forecasting is done for 212 days from the beginning of 2017 and past data from 2016 is used. For forecasting, Autoregressive Integrated Moving Average (ARIMA), Trigonometr...
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| Format: | Article |
| Language: | English |
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MDPI AG
2019-03-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/12/5/928 |
| _version_ | 1828152371972145152 |
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| author | Orhan Altuğ Karabiber George Xydis |
| author_facet | Orhan Altuğ Karabiber George Xydis |
| author_sort | Orhan Altuğ Karabiber |
| collection | DOAJ |
| description | In this paper day-ahead electricity price forecasting for the Denmark-West region is realized with a 24 h forecasting range. The forecasting is done for 212 days from the beginning of 2017 and past data from 2016 is used. For forecasting, Autoregressive Integrated Moving Average (ARIMA), Trigonometric Seasonal Box-Cox Transformation with ARMA residuals Trend and Seasonal Components (TBATS) and Artificial Neural Networks (ANN) methods are used and seasonal naïve forecast is utilized as a benchmark. Mean absolute error (MAE) and root mean squared error (RMSE) are used as accuracy criterions. ARIMA and ANN are utilized with external variables and variable analysis is realized in order to improve forecasting results. As a result of variable analysis, it was observed that excluding temperature from external variables helped improve forecasting results. In terms of mean error ARIMA yielded the best results while ANN had the lowest minimum error and standard deviation. TBATS performed better than ANN in terms of mean error. To further improve forecasting accuracy, the three forecasts were combined using simple averaging and ANN methods and they were both found to be beneficial, with simple averaging having better accuracy. Overall, this paper demonstrates a solid forecasting methodology, while showing actual forecasting results and improvements for different forecasting methods. |
| first_indexed | 2024-04-11T22:10:29Z |
| format | Article |
| id | doaj.art-1cc609aaa7104d21b78efc24410b9429 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T22:10:29Z |
| publishDate | 2019-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-1cc609aaa7104d21b78efc24410b94292022-12-22T04:00:34ZengMDPI AGEnergies1996-10732019-03-0112592810.3390/en12050928en12050928Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA MethodsOrhan Altuğ Karabiber0George Xydis1Department of Business Development and Technology, Aarhus University, Birk Centerpark 15, 7400 Herning, DenmarkDepartment of Business Development and Technology, Aarhus University, Birk Centerpark 15, 7400 Herning, DenmarkIn this paper day-ahead electricity price forecasting for the Denmark-West region is realized with a 24 h forecasting range. The forecasting is done for 212 days from the beginning of 2017 and past data from 2016 is used. For forecasting, Autoregressive Integrated Moving Average (ARIMA), Trigonometric Seasonal Box-Cox Transformation with ARMA residuals Trend and Seasonal Components (TBATS) and Artificial Neural Networks (ANN) methods are used and seasonal naïve forecast is utilized as a benchmark. Mean absolute error (MAE) and root mean squared error (RMSE) are used as accuracy criterions. ARIMA and ANN are utilized with external variables and variable analysis is realized in order to improve forecasting results. As a result of variable analysis, it was observed that excluding temperature from external variables helped improve forecasting results. In terms of mean error ARIMA yielded the best results while ANN had the lowest minimum error and standard deviation. TBATS performed better than ANN in terms of mean error. To further improve forecasting accuracy, the three forecasts were combined using simple averaging and ANN methods and they were both found to be beneficial, with simple averaging having better accuracy. Overall, this paper demonstrates a solid forecasting methodology, while showing actual forecasting results and improvements for different forecasting methods.http://www.mdpi.com/1996-1073/12/5/928electricity price forecastingday ahead marketforecast combinationARIMAneural network |
| spellingShingle | Orhan Altuğ Karabiber George Xydis Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods Energies electricity price forecasting day ahead market forecast combination ARIMA neural network |
| title | Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods |
| title_full | Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods |
| title_fullStr | Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods |
| title_full_unstemmed | Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods |
| title_short | Electricity Price Forecasting in the Danish Day-Ahead Market Using the TBATS, ANN and ARIMA Methods |
| title_sort | electricity price forecasting in the danish day ahead market using the tbats ann and arima methods |
| topic | electricity price forecasting day ahead market forecast combination ARIMA neural network |
| url | http://www.mdpi.com/1996-1073/12/5/928 |
| work_keys_str_mv | AT orhanaltugkarabiber electricitypriceforecastinginthedanishdayaheadmarketusingthetbatsannandarimamethods AT georgexydis electricitypriceforecastinginthedanishdayaheadmarketusingthetbatsannandarimamethods |