A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in China
Abstract With the rapid development of re‐electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-09-01
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| Series: | IET Renewable Power Generation |
| Online Access: | https://doi.org/10.1049/rpg2.12373 |
| _version_ | 1828327340664422400 |
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| author | Xinyue Zhao Jianxiao Wang Tiance Zhang Da Cui Gengyin Li Ming Zhou |
| author_facet | Xinyue Zhao Jianxiao Wang Tiance Zhang Da Cui Gengyin Li Ming Zhou |
| author_sort | Xinyue Zhao |
| collection | DOAJ |
| description | Abstract With the rapid development of re‐electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number of redundant features may lead to the overfitting of the forecasting engine. To enhance the performance of extreme learning machine (ELM) under massive data scale, this paper presents a kernel extreme learning machine (KELM) based method which can be used for short‐term load prediction. First, a feature dimensionality reduction is performed using a kernelized principal component analysis, which aims to eliminate redundant input vectors. Then, the hyperparameters of KELM are optimized to improve the prediction accuracy and generalization. Case studies based on a province‐level power system in China demonstrate that the presented method can significantly improve the accuracy of load forecasting by 3.14% in contrast to traditional ELM. |
| first_indexed | 2024-04-13T19:54:06Z |
| format | Article |
| id | doaj.art-d92d0dbb3690436c917b87bc7ac86881 |
| institution | Directory Open Access Journal |
| issn | 1752-1416 1752-1424 |
| language | English |
| last_indexed | 2024-04-13T19:54:06Z |
| publishDate | 2022-09-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj.art-d92d0dbb3690436c917b87bc7ac868812022-12-22T02:32:25ZengWileyIET Renewable Power Generation1752-14161752-14242022-09-0116122658266610.1049/rpg2.12373A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in ChinaXinyue Zhao0Jianxiao Wang1Tiance Zhang2Da Cui3Gengyin Li4Ming Zhou5State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaNational Power Dispatching and Control Center State Grid Corporation of China Beijing ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaState Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources School of Electrical and Electronic Engineering North China Electric Power University Beijing ChinaAbstract With the rapid development of re‐electrification, traditional load forecasting faces a significant increase of influencing factors. Existing literature focuses on examining the influencing factors related to load profiles in order to improve the prediction accuracy. However, a large number of redundant features may lead to the overfitting of the forecasting engine. To enhance the performance of extreme learning machine (ELM) under massive data scale, this paper presents a kernel extreme learning machine (KELM) based method which can be used for short‐term load prediction. First, a feature dimensionality reduction is performed using a kernelized principal component analysis, which aims to eliminate redundant input vectors. Then, the hyperparameters of KELM are optimized to improve the prediction accuracy and generalization. Case studies based on a province‐level power system in China demonstrate that the presented method can significantly improve the accuracy of load forecasting by 3.14% in contrast to traditional ELM.https://doi.org/10.1049/rpg2.12373 |
| spellingShingle | Xinyue Zhao Jianxiao Wang Tiance Zhang Da Cui Gengyin Li Ming Zhou A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in China IET Renewable Power Generation |
| title | A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in China |
| title_full | A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in China |
| title_fullStr | A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in China |
| title_full_unstemmed | A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in China |
| title_short | A novel short‐term load forecasting approach based on kernel extreme learning machine: A provincial case in China |
| title_sort | novel short term load forecasting approach based on kernel extreme learning machine a provincial case in china |
| url | https://doi.org/10.1049/rpg2.12373 |
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