Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm
Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), ker...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2021-12-01
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| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/11/24/12014 |
| _version_ | 1797506721185267712 |
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| author | Yingying Fan Haichao Wang Xinyue Zhao Qiaoran Yang Yi Liang |
| author_facet | Yingying Fan Haichao Wang Xinyue Zhao Qiaoran Yang Yi Liang |
| author_sort | Yingying Fan |
| collection | DOAJ |
| description | Accurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), kernel extreme learning machine (KELM) and fireworks algorithm (FWA) is proposed. First, KPCA modal is used to reduce the dimension of the feature, thus redundant input samples are merged. Next, FWA is employed to optimize the parameters C and σ of KELM. Lastly, the load forecasting modal of KPCA-FWA-KELM is established. The relevant data of a distributed energy system in Beijing, China, is selected for training test to verify the effectiveness of the proposed method. The results show that the new hybrid KPCA-FWA-KELM method has superior performance, robustness and versatility in load prediction of distributed energy systems. |
| first_indexed | 2024-03-10T04:36:41Z |
| format | Article |
| id | doaj.art-73d6c1a963954cf1ab6bc03bb6ea8213 |
| institution | Directory Open Access Journal |
| issn | 2076-3417 |
| language | English |
| last_indexed | 2024-03-10T04:36:41Z |
| publishDate | 2021-12-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj.art-73d6c1a963954cf1ab6bc03bb6ea82132023-11-23T03:41:53ZengMDPI AGApplied Sciences2076-34172021-12-0111241201410.3390/app112412014Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks AlgorithmYingying Fan0Haichao Wang1Xinyue Zhao2Qiaoran Yang3Yi Liang4School of Art, Hebei GEO University, Shijiazhuang 050031, ChinaLong Yuan (Beijing) Wind Power Engineering & Consulting Co., Ltd., Beijing 100034, ChinaSchool of Electrical and Electronic Engineering, North China Electric Power University, Beijing 102206, ChinaSchool of Management, Hebei GEO University, Shijiazhuang 050031, ChinaStrategy and Management Base of Mineral Resources in Hebei Province, Hebei GEO University, Shijiazhuang 050031, ChinaAccurate and stable load forecasting has great significance to ensure the safe operation of distributed energy system. For the purpose of improving the accuracy and stability of distributed energy system load forecasting, a forecasting model in view of kernel principal component analysis (KPCA), kernel extreme learning machine (KELM) and fireworks algorithm (FWA) is proposed. First, KPCA modal is used to reduce the dimension of the feature, thus redundant input samples are merged. Next, FWA is employed to optimize the parameters C and σ of KELM. Lastly, the load forecasting modal of KPCA-FWA-KELM is established. The relevant data of a distributed energy system in Beijing, China, is selected for training test to verify the effectiveness of the proposed method. The results show that the new hybrid KPCA-FWA-KELM method has superior performance, robustness and versatility in load prediction of distributed energy systems.https://www.mdpi.com/2076-3417/11/24/12014load forecasting of distributed energy systemkernel principal component analysisfireworks algorithmextreme learning machine with kernel |
| spellingShingle | Yingying Fan Haichao Wang Xinyue Zhao Qiaoran Yang Yi Liang Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm Applied Sciences load forecasting of distributed energy system kernel principal component analysis fireworks algorithm extreme learning machine with kernel |
| title | Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm |
| title_full | Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm |
| title_fullStr | Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm |
| title_full_unstemmed | Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm |
| title_short | Short-Term Load Forecasting of Distributed Energy System Based on Kernel Principal Component Analysis and KELM Optimized by Fireworks Algorithm |
| title_sort | short term load forecasting of distributed energy system based on kernel principal component analysis and kelm optimized by fireworks algorithm |
| topic | load forecasting of distributed energy system kernel principal component analysis fireworks algorithm extreme learning machine with kernel |
| url | https://www.mdpi.com/2076-3417/11/24/12014 |
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