Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours
To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-07-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/11/7/1899 |
| _version_ | 1811302057276080128 |
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| author | Lin Lin Lin Xue Zhiqiang Hu Nantian Huang |
| author_facet | Lin Lin Lin Xue Zhiqiang Hu Nantian Huang |
| author_sort | Lin Lin |
| collection | DOAJ |
| description | To improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting. |
| first_indexed | 2024-04-13T07:21:34Z |
| format | Article |
| id | doaj.art-3f7278ff96994a63a54c013fda3e72cb |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-13T07:21:34Z |
| publishDate | 2018-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-3f7278ff96994a63a54c013fda3e72cb2022-12-22T02:56:36ZengMDPI AGEnergies1996-10732018-07-01117189910.3390/en11071899en11071899Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different HoursLin Lin0Lin Xue1Zhiqiang Hu2Nantian Huang3College of Information and Control Engineering, Jilin Institute of Chemical Technology, Jilin 132022, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin 132013, ChinaZhejiang Electric Power Corporation Wenzhou Power Supply Company, Wenzhou 325000, ChinaSchool of Electrical Engineering, Northeast Electric Power University, Jilin 132013, ChinaTo improve the accuracy of the day-ahead load forecasting predictions of a single model, a novel modular parallel forecasting model with feature selection was proposed. First, load features were extracted from a historic load with a horizon from the previous 24 h to the previous 168 h considering the calendar feature. Second, a feature selection combined with a predictor process was carried out to select the optimal feature for building a reliable predictor with respect to each hour. The final modular model consisted of 24 predictors with a respective optimal feature subset for day-ahead load forecasting. New England and Singapore load data were used to evaluate the effectiveness of the proposed method. The results indicated that the accuracy of the proposed modular model was higher than that of the traditional method. Furthermore, conducting a feature selection step when building a predictor improved the accuracy of load forecasting.http://www.mdpi.com/1996-1073/11/7/1899day-ahead load forecastingmodular predictorfeature selection |
| spellingShingle | Lin Lin Lin Xue Zhiqiang Hu Nantian Huang Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours Energies day-ahead load forecasting modular predictor feature selection |
| title | Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours |
| title_full | Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours |
| title_fullStr | Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours |
| title_full_unstemmed | Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours |
| title_short | Modular Predictor for Day-Ahead Load Forecasting and Feature Selection for Different Hours |
| title_sort | modular predictor for day ahead load forecasting and feature selection for different hours |
| topic | day-ahead load forecasting modular predictor feature selection |
| url | http://www.mdpi.com/1996-1073/11/7/1899 |
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