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Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion
In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512605/ https://www.ncbi.nlm.nih.gov/pubmed/33265203 http://dx.doi.org/10.3390/e20020112 |
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author | Duan, Jiandong Qiu, Xinyu Ma, Wentao Tian, Xuan Shang, Di |
author_facet | Duan, Jiandong Qiu, Xinyu Ma, Wentao Tian, Xuan Shang, Di |
author_sort | Duan, Jiandong |
collection | PubMed |
description | In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model. |
format | Online Article Text |
id | pubmed-7512605 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-75126052020-11-09 Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion Duan, Jiandong Qiu, Xinyu Ma, Wentao Tian, Xuan Shang, Di Entropy (Basel) Article In recent years, with the deepening of China’s electricity sales side reform and electricity market opening up gradually, the forecasting of electricity consumption (FoEC) becomes an extremely important technique for the electricity market. At present, how to forecast the electricity accurately and make an evaluation of results scientifically are still key research topics. In this paper, we propose a novel prediction scheme based on the least-square support vector machine (LSSVM) model with a maximum correntropy criterion (MCC) to forecast the electricity consumption (EC). Firstly, the electricity characteristics of various industries are analyzed to determine the factors that mainly affect the changes in electricity, such as the gross domestic product (GDP), temperature, and so on. Secondly, according to the statistics of the status quo of the small sample data, the LSSVM model is employed as the prediction model. In order to optimize the parameters of the LSSVM model, we further use the local similarity function MCC as the evaluation criterion. Thirdly, we employ the K-fold cross-validation and grid searching methods to improve the learning ability. In the experiments, we have used the EC data of Shaanxi Province in China to evaluate the proposed prediction scheme, and the results show that the proposed prediction scheme outperforms the method based on the traditional LSSVM model. MDPI 2018-02-08 /pmc/articles/PMC7512605/ /pubmed/33265203 http://dx.doi.org/10.3390/e20020112 Text en © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Article Duan, Jiandong Qiu, Xinyu Ma, Wentao Tian, Xuan Shang, Di Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion |
title | Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion |
title_full | Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion |
title_fullStr | Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion |
title_full_unstemmed | Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion |
title_short | Electricity Consumption Forecasting Scheme via Improved LSSVM with Maximum Correntropy Criterion |
title_sort | electricity consumption forecasting scheme via improved lssvm with maximum correntropy criterion |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7512605/ https://www.ncbi.nlm.nih.gov/pubmed/33265203 http://dx.doi.org/10.3390/e20020112 |
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