Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term
The uncertainty and variability in electricity market price (EMP) signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-08-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/9/693 |
| _version_ | 1828138811425554432 |
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| author | Gerardo J. Osório Jorge N. D. L. Gonçalves Juan M. Lujano-Rojas João P. S. Catalão |
| author_facet | Gerardo J. Osório Jorge N. D. L. Gonçalves Juan M. Lujano-Rojas João P. S. Catalão |
| author_sort | Gerardo J. Osório |
| collection | DOAJ |
| description | The uncertainty and variability in electricity market price (EMP) signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT), differential evolutionary particle swarm optimization (DEEPSO), and an adaptive neuro-fuzzy inference system (ANFIS) to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment. |
| first_indexed | 2024-04-11T18:43:16Z |
| format | Article |
| id | doaj.art-b32f846934554935bbb57684b9b0915f |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T18:43:16Z |
| publishDate | 2016-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-b32f846934554935bbb57684b9b0915f2022-12-22T04:08:56ZengMDPI AGEnergies1996-10732016-08-019969310.3390/en9090693en9090693Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-TermGerardo J. Osório0Jorge N. D. L. Gonçalves1Juan M. Lujano-Rojas2João P. S. Catalão3C-MAST, University of Beira Interior, Covilhã 6201-001, PortugalINESC TEC and the Faculty of Engineering of the University of Porto, Porto 4200-465, PortugalC-MAST, University of Beira Interior, Covilhã 6201-001, PortugalC-MAST, University of Beira Interior, Covilhã 6201-001, PortugalThe uncertainty and variability in electricity market price (EMP) signals and players’ behavior, as well as in renewable power generation, especially wind power, pose considerable challenges. Hence, enhancement of forecasting approaches is required for all electricity market players to deal with the non-stationary and stochastic nature of such time series, making it possible to accurately support their decisions in a competitive environment with lower forecasting error and with an acceptable computational time. As previously published methodologies have shown, hybrid approaches are good candidates to overcome most of the previous concerns about time-series forecasting. In this sense, this paper proposes an enhanced hybrid approach composed of an innovative combination of wavelet transform (WT), differential evolutionary particle swarm optimization (DEEPSO), and an adaptive neuro-fuzzy inference system (ANFIS) to forecast EMP signals in different electricity markets and wind power in Portugal, in the short-term, considering only historical data. Test results are provided by comparing with other reported studies, demonstrating the proficiency of the proposed hybrid approach in a real environment.http://www.mdpi.com/1996-1073/9/9/693adaptive neuro-fuzzy inference system (ANFIS)differential evolutionary particle swarm optimization (DEEPSO)electricity market prices (EMP)forecastingshort-termtime serieswavelet transform (WT)wind power |
| spellingShingle | Gerardo J. Osório Jorge N. D. L. Gonçalves Juan M. Lujano-Rojas João P. S. Catalão Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term Energies adaptive neuro-fuzzy inference system (ANFIS) differential evolutionary particle swarm optimization (DEEPSO) electricity market prices (EMP) forecasting short-term time series wavelet transform (WT) wind power |
| title | Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term |
| title_full | Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term |
| title_fullStr | Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term |
| title_full_unstemmed | Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term |
| title_short | Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term |
| title_sort | enhanced forecasting approach for electricity market prices and wind power data series in the short term |
| topic | adaptive neuro-fuzzy inference system (ANFIS) differential evolutionary particle swarm optimization (DEEPSO) electricity market prices (EMP) forecasting short-term time series wavelet transform (WT) wind power |
| url | http://www.mdpi.com/1996-1073/9/9/693 |
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