A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting
The paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the proposed forecaster is compared with forecasters base...
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/14/24/8455 |
| _version_ | 1797505060220960768 |
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| author | Ankit Kumar Srivastava Ajay Shekhar Pandey Rajvikram Madurai Elavarasan Umashankar Subramaniam Saad Mekhilef Lucian Mihet-Popa |
| author_facet | Ankit Kumar Srivastava Ajay Shekhar Pandey Rajvikram Madurai Elavarasan Umashankar Subramaniam Saad Mekhilef Lucian Mihet-Popa |
| author_sort | Ankit Kumar Srivastava |
| collection | DOAJ |
| description | The paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the proposed forecaster is compared with forecasters based on classification tree and regression tree. A hybrid FS method based on the elitist genetic algorithm (GA) and a tree-based method is applied for FS. Making use of selected features, aperformance test of the forecaster was carried out to establish the usefulness of the proposed approach. By way of analyzing and forecasts for day-ahead electricity prices in the Australian electricity markets, the proposed approach is evaluated and it has been established that, with the selected feature, the proposed forecaster consistently outperforms the forecaster with a larger feature set. The proposed method is simulated in MATLAB and WEKA software. |
| first_indexed | 2024-03-10T04:13:17Z |
| format | Article |
| id | doaj.art-a8682091aa194c529b4f2ad1b20dfaaf |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T04:13:17Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a8682091aa194c529b4f2ad1b20dfaaf2023-11-23T08:07:32ZengMDPI AGEnergies1996-10732021-12-011424845510.3390/en14248455A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price ForecastingAnkit Kumar Srivastava0Ajay Shekhar Pandey1Rajvikram Madurai Elavarasan2Umashankar Subramaniam3Saad Mekhilef4Lucian Mihet-Popa5Department of Electrical Engineering, Dr. Rammanohar Lohia Avdh University, Ayodhya 224001, IndiaDepartment of Electrical Engineering, Kamla Nehru Institute of Technology, Sultanpur 228118, IndiaDepartment of Electrical and Electronics Engineering, Thiagarajar College of Engineering, Madurai 625015, IndiaRenewable Energy Laboratory, Department of Communications and Networks, College of Engineering, Prince Sultan University, Riyadh 11586, Saudi ArabiaSchool of Science, Computing and Engineering Technologies, Swinburne University of Technology, Hawthorn, VIC 3122, AustraliaFaculty of Electrical Engineering, Ostfold University College, 1757 Halden, NorwayThe paper proposes a novel hybrid feature selection (FS) method for day-ahead electricity price forecasting. The work presents a novel hybrid FS algorithm for obtaining optimal feature set to gain optimal forecast accuracy. The performance of the proposed forecaster is compared with forecasters based on classification tree and regression tree. A hybrid FS method based on the elitist genetic algorithm (GA) and a tree-based method is applied for FS. Making use of selected features, aperformance test of the forecaster was carried out to establish the usefulness of the proposed approach. By way of analyzing and forecasts for day-ahead electricity prices in the Australian electricity markets, the proposed approach is evaluated and it has been established that, with the selected feature, the proposed forecaster consistently outperforms the forecaster with a larger feature set. The proposed method is simulated in MATLAB and WEKA software.https://www.mdpi.com/1996-1073/14/24/8455price forecastingfeature selectionelitist genetic algorithmSMO regressionconfidence interval |
| spellingShingle | Ankit Kumar Srivastava Ajay Shekhar Pandey Rajvikram Madurai Elavarasan Umashankar Subramaniam Saad Mekhilef Lucian Mihet-Popa A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting Energies price forecasting feature selection elitist genetic algorithm SMO regression confidence interval |
| title | A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting |
| title_full | A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting |
| title_fullStr | A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting |
| title_full_unstemmed | A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting |
| title_short | A Novel Hybrid Feature Selection Method for Day-Ahead Electricity Price Forecasting |
| title_sort | novel hybrid feature selection method for day ahead electricity price forecasting |
| topic | price forecasting feature selection elitist genetic algorithm SMO regression confidence interval |
| url | https://www.mdpi.com/1996-1073/14/24/8455 |
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