Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method
In the present liberalized energy markets, electricity demand forecasting is critical for planning of generation capacity and required resources. An accurate and efficient electricity demand forecast can reduce the risk of power outages and excessive power generation. Avoiding blackouts is crucial f...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-09-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/18/6675 |
| _version_ | 1797580294325272576 |
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| author | Hasnain Iftikhar Josue E. Turpo-Chaparro Paulo Canas Rodrigues Javier Linkolk López-Gonzales |
| author_facet | Hasnain Iftikhar Josue E. Turpo-Chaparro Paulo Canas Rodrigues Javier Linkolk López-Gonzales |
| author_sort | Hasnain Iftikhar |
| collection | DOAJ |
| description | In the present liberalized energy markets, electricity demand forecasting is critical for planning of generation capacity and required resources. An accurate and efficient electricity demand forecast can reduce the risk of power outages and excessive power generation. Avoiding blackouts is crucial for economic growth, and electricity is an essential energy source for industry. Considering these facts, this study presents a detailed analysis of the forecast of hourly electricity demand by comparing novel decomposition methods with several univariate and multivariate time series models. To that end, we use the three proposed decomposition methods to divide the electricity demand time series into the following subseries: a long-run linear trend, a seasonal trend, and a stochastic trend. Next, each subseries is forecast using all conceivable combinations of univariate and multivariate time series models. Finally, the multiple forecasting models are immediately integrated to provide a final one-day-ahead electricity demand forecast. The presented modeling and forecasting technique is implemented for the Nord Pool electricity market’s hourly electricity demand. Three accuracy indicators, a statistical test, and a graphical analysis are used to assess the performance of the proposed decomposition combination forecasting technique. Hence, the forecasting results demonstrate the efficiency and precision of the proposed decomposition combination forecasting technique. In addition, the final best combination model within the proposed forecasting framework is comparatively better than the best models proposed in the literature and standard benchmark models. Finally, we suggest that the decomposition combination forecasting approach developed in this study be employed to handle additional complicated power market forecasting challenges. |
| first_indexed | 2024-03-10T22:49:00Z |
| format | Article |
| id | doaj.art-2ee77f5ae75f4eeeb67bfd6156f88106 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T22:49:00Z |
| publishDate | 2023-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-2ee77f5ae75f4eeeb67bfd6156f881062023-11-19T10:28:31ZengMDPI AGEnergies1996-10732023-09-011618667510.3390/en16186675Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination MethodHasnain Iftikhar0Josue E. Turpo-Chaparro1Paulo Canas Rodrigues2Javier Linkolk López-Gonzales3Department of Statistics, Quaid-i-Azam University, Islamabad 45320, PakistanEscuela de Posgrado, Universidad Peruana Unión, Lima 15468, PeruDepartment of Statistics, Federal University of Bahia, Salvador 40170-110, BrazilVicerrectorado de Investigación, Universidad Privada Norbert Wiener, Lima 15046, PeruIn the present liberalized energy markets, electricity demand forecasting is critical for planning of generation capacity and required resources. An accurate and efficient electricity demand forecast can reduce the risk of power outages and excessive power generation. Avoiding blackouts is crucial for economic growth, and electricity is an essential energy source for industry. Considering these facts, this study presents a detailed analysis of the forecast of hourly electricity demand by comparing novel decomposition methods with several univariate and multivariate time series models. To that end, we use the three proposed decomposition methods to divide the electricity demand time series into the following subseries: a long-run linear trend, a seasonal trend, and a stochastic trend. Next, each subseries is forecast using all conceivable combinations of univariate and multivariate time series models. Finally, the multiple forecasting models are immediately integrated to provide a final one-day-ahead electricity demand forecast. The presented modeling and forecasting technique is implemented for the Nord Pool electricity market’s hourly electricity demand. Three accuracy indicators, a statistical test, and a graphical analysis are used to assess the performance of the proposed decomposition combination forecasting technique. Hence, the forecasting results demonstrate the efficiency and precision of the proposed decomposition combination forecasting technique. In addition, the final best combination model within the proposed forecasting framework is comparatively better than the best models proposed in the literature and standard benchmark models. Finally, we suggest that the decomposition combination forecasting approach developed in this study be employed to handle additional complicated power market forecasting challenges.https://www.mdpi.com/1996-1073/16/18/6675Nord Pool electricity marketday-ahead electricity demand forecastingdecomposition combination methodunivariate and multivariate times series models |
| spellingShingle | Hasnain Iftikhar Josue E. Turpo-Chaparro Paulo Canas Rodrigues Javier Linkolk López-Gonzales Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method Energies Nord Pool electricity market day-ahead electricity demand forecasting decomposition combination method univariate and multivariate times series models |
| title | Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method |
| title_full | Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method |
| title_fullStr | Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method |
| title_full_unstemmed | Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method |
| title_short | Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method |
| title_sort | day ahead electricity demand forecasting using a novel decomposition combination method |
| topic | Nord Pool electricity market day-ahead electricity demand forecasting decomposition combination method univariate and multivariate times series models |
| url | https://www.mdpi.com/1996-1073/16/18/6675 |
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