Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique
Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new dec...
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2023
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| author | Iftikhar, Hasnain Turpo Chaparro, Josue Edison Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_browse | Canas Rodrigues, Paulo Iftikhar, Hasnain López Gonzales, Javier Linkolk Turpo Chaparro, Josue Edison |
| author_facet | Iftikhar, Hasnain Turpo Chaparro, Josue Edison Canas Rodrigues, Paulo López Gonzales, Javier Linkolk |
| author_sort | Iftikhar, Hasnain |
| collection | Producción científica |
| description | Over the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods and various time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. For this purpose, first, the time-series of the original electricity prices deals with the treatment of extreme values. Second, the filtered series of the electricity prices is decomposed into three new subseries, namely the long-term trend, a seasonal series, and a residual series, using two new proposed decomposition methods. Third, we forecast each subseries using different univariate and multivariate time-series models and all possible combinations. Finally, the individual forecasting models are combined directly to obtain the final one-day-ahead price forecast. The proposed decomposition–combination forecasting technique is applied to hourly spot electricity prices from the Italian electricity-market data from 1 January 2014 to 31 December 2019. Hence, four different accuracy mean errors—mean absolute error, mean squared absolute percent error, root mean squared error, and mean absolute percent error; a statistical test, the Diebold–Marino test; and graphical analysis—are determined to check the performance of the proposed decomposition–combination forecasting method. The experimental findings (mean errors, statistical test, and graphical analysis) show that the proposed forecasting method is effective and accurate in day-ahead electricity price forecasting. Additionally, our forecasting outcomes are comparable to those described in the literature and are regarded as standard benchmark models. Finally, the authors recommended that the proposed decomposition–combination forecasting technique in this research work be applied to other complicated energy market forecasting challenges. |
| format | Article |
| id | oai:cris.upeu.edu.pe:20.500.15037/427 |
| institution | UPeU |
| language | English |
| publishDate | 2023 |
| publishDateRange | 2023 |
| publishDateSort | 2023 |
| record_format | dspace |
| spelling | oai:cris.upeu.edu.pe:20.500.15037/4272026-10-30T16:14:37ZForecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination TechniqueIftikhar, HasnainTurpo Chaparro, Josue EdisonCanas Rodrigues, PauloLópez Gonzales, Javier LinkolkItalian electricity marketday-ahead electricity prices forecastingnonparametric regression methodstimes series modelsdecomposition–combination techniqueOver the last 30 years, day-ahead electricity price forecasts have been critical to public and private decision-making. This importance has increased since the global wave of deregulation and liberalization in the energy sector at the end of the 1990s. Given these facts, this work presents a new decomposition–combination technique that employs several nonparametric regression methods and various time-series models to enhance the accuracy and efficiency of day-ahead electricity price forecasting. For this purpose, first, the time-series of the original electricity prices deals with the treatment of extreme values. Second, the filtered series of the electricity prices is decomposed into three new subseries, namely the long-term trend, a seasonal series, and a residual series, using two new proposed decomposition methods. Third, we forecast each subseries using different univariate and multivariate time-series models and all possible combinations. Finally, the individual forecasting models are combined directly to obtain the final one-day-ahead price forecast. The proposed decomposition–combination forecasting technique is applied to hourly spot electricity prices from the Italian electricity-market data from 1 January 2014 to 31 December 2019. Hence, four different accuracy mean errors—mean absolute error, mean squared absolute percent error, root mean squared error, and mean absolute percent error; a statistical test, the Diebold–Marino test; and graphical analysis—are determined to check the performance of the proposed decomposition–combination forecasting method. The experimental findings (mean errors, statistical test, and graphical analysis) show that the proposed forecasting method is effective and accurate in day-ahead electricity price forecasting. Additionally, our forecasting outcomes are comparable to those described in the literature and are regarded as standard benchmark models. Finally, the authors recommended that the proposed decomposition–combination forecasting technique in this research work be applied to other complicated energy market forecasting challenges.Articlehttps://www.mdpi.com/1996-1073/16/18/6669/pdf?version=1695005075https://doi.org/10.3390/en1618666910.3390/en16186669https://hdl.handle.net/20.500.15037/427W4386838433enEnergies1996-10732023-09-172026-06-13T08:13:21Z2026-06-13T08:13:21Z |
| spellingShingle | Italian electricity market day-ahead electricity prices forecasting nonparametric regression methods times series models decomposition–combination technique Iftikhar, Hasnain Turpo Chaparro, Josue Edison Canas Rodrigues, Paulo López Gonzales, Javier Linkolk Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique |
| title | Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique |
| title_full | Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique |
| title_fullStr | Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique |
| title_full_unstemmed | Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique |
| title_short | Forecasting Day-Ahead Electricity Prices for the Italian Electricity Market Using a New Decomposition—Combination Technique |
| title_sort | forecasting day ahead electricity prices for the italian electricity market using a new decomposition combination technique |
| topic | Italian electricity market day-ahead electricity prices forecasting nonparametric regression methods times series models decomposition–combination technique |
| topic_facet | Italian electricity market day-ahead electricity prices forecasting nonparametric regression methods times series models decomposition–combination technique |
| url | https://www.mdpi.com/1996-1073/16/18/6669/pdf?version=1695005075 https://doi.org/10.3390/en16186669 https://hdl.handle.net/20.500.15037/427 |
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