A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination
Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2015-12-01
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| Series: | Energies |
| Subjects: | |
| Online Access: | http://www.mdpi.com/1996-1073/9/1/20 |
| _version_ | 1817990720391217152 |
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| author | Lianhui Li Chunyang Mu Shaohu Ding Zheng Wang Runyang Mo Yongfeng Song |
| author_facet | Lianhui Li Chunyang Mu Shaohu Ding Zheng Wang Runyang Mo Yongfeng Song |
| author_sort | Lianhui Li |
| collection | DOAJ |
| description | Medium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method. |
| first_indexed | 2024-04-14T01:03:41Z |
| format | Article |
| id | doaj.art-d79ca8bdd4a743cfbfd6b536602c74f3 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-14T01:03:41Z |
| publishDate | 2015-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-d79ca8bdd4a743cfbfd6b536602c74f32022-12-22T02:21:20ZengMDPI AGEnergies1996-10732015-12-01912010.3390/en9010020en9010020A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight DeterminationLianhui Li0Chunyang Mu1Shaohu Ding2Zheng Wang3Runyang Mo4Yongfeng Song5College of Mechatronic Engineering, Beifang University of Nationalities, Yinchuan 750021, ChinaState Key Laboratory of Robotics and System, Harbin Institute of Technology (HIT), Harbin 150001, ChinaCollege of Mechatronic Engineering, Beifang University of Nationalities, Yinchuan 750021, ChinaState Grid Ningxia Electric Power Design Co. Ltd., Yinchuan 750001, ChinaSchool of Management, Qingdao Technological University, Qingdao 266520, ChinaSchool of Management, Qingdao Technological University, Qingdao 266520, ChinaMedium-and-long-term load forecasting plays an important role in energy policy implementation and electric department investment decision. Aiming to improve the robustness and accuracy of annual electric load forecasting, a robust weighted combination load forecasting method based on forecast model filtering and adaptive variable weight determination is proposed. Similar years of selection is carried out based on the similarity between the history year and the forecast year. The forecast models are filtered to select the better ones according to their comprehensive validity degrees. To determine the adaptive variable weight of the selected forecast models, the disturbance variable is introduced into Immune Algorithm-Particle Swarm Optimization (IA-PSO) and the adaptive adjustable strategy of particle search speed is established. Based on the forecast model weight determined by improved IA-PSO, the weighted combination forecast of annual electric load is obtained. The given case study illustrates the correctness and feasibility of the proposed method.http://www.mdpi.com/1996-1073/9/1/20load forecastingrobustnesscombination forecastMarkov chainnormal cloud modelimmune algorithmparticle swarm optimization |
| spellingShingle | Lianhui Li Chunyang Mu Shaohu Ding Zheng Wang Runyang Mo Yongfeng Song A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination Energies load forecasting robustness combination forecast Markov chain normal cloud model immune algorithm particle swarm optimization |
| title | A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination |
| title_full | A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination |
| title_fullStr | A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination |
| title_full_unstemmed | A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination |
| title_short | A Robust Weighted Combination Forecasting Method Based on Forecast Model Filtering and Adaptive Variable Weight Determination |
| title_sort | robust weighted combination forecasting method based on forecast model filtering and adaptive variable weight determination |
| topic | load forecasting robustness combination forecast Markov chain normal cloud model immune algorithm particle swarm optimization |
| url | http://www.mdpi.com/1996-1073/9/1/20 |
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