Data Pre-processing Method Based on Distorted Data Noise Reduction and Its Application in Wind Power Prediction

Improving the accuracy of wind power data is of great significance for building ubiquitous power internet of things (UPIoT). Wind power prediction has a high demand for historical data sets. Most of research was focused on improving the prediction accuracy by establishing different prediction models...

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Main Authors: Xincheng JIN, Xiuyuan YANG
Format: Article
Language:English
Published: Editorial Department of Power Generation Technology 2020-08-01
Series:发电技术
Subjects:
Online Access:https://www.pgtjournal.com/EN/10.12096/j.2096-4528.pgt.19035
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author Xincheng JIN
Xiuyuan YANG
author_facet Xincheng JIN
Xiuyuan YANG
author_sort Xincheng JIN
collection DOAJ
description Improving the accuracy of wind power data is of great significance for building ubiquitous power internet of things (UPIoT). Wind power prediction has a high demand for historical data sets. Most of research was focused on improving the prediction accuracy by establishing different prediction models or proposing different prediction algorithms. There is not much attention on noise data elimination. Thus, a noise reduction method for the historical wind power data was proposed, which was mainly applied to the data set, by eliminating the distorted data in the historical wind power data, the amount of useless data was reduced, the accuracy of wind power prediction was improved, and the data modeling and prediction time was shortened.
format Article
id doaj-art-24df710111fe4e9a841980ba3d644523
institution Kabale University
issn 2096-4528
language English
publishDate 2020-08-01
publisher Editorial Department of Power Generation Technology
record_format Article
series 发电技术
spelling doaj-art-24df710111fe4e9a841980ba3d6445232024-11-09T02:11:44ZengEditorial Department of Power Generation Technology发电技术2096-45282020-08-0141444745110.12096/j.2096-4528.pgt.19035fdjs-41-4-447Data Pre-processing Method Based on Distorted Data Noise Reduction and Its Application in Wind Power PredictionXincheng JIN0Xiuyuan YANG1State Grid Beijing Yizhuang Power Supply Company, Daxing District, Beijing 100176, ChinaSchool of Automation, Beijing Information Science & Technology University, Haidian District, Beijing 100192, ChinaImproving the accuracy of wind power data is of great significance for building ubiquitous power internet of things (UPIoT). Wind power prediction has a high demand for historical data sets. Most of research was focused on improving the prediction accuracy by establishing different prediction models or proposing different prediction algorithms. There is not much attention on noise data elimination. Thus, a noise reduction method for the historical wind power data was proposed, which was mainly applied to the data set, by eliminating the distorted data in the historical wind power data, the amount of useless data was reduced, the accuracy of wind power prediction was improved, and the data modeling and prediction time was shortened.https://www.pgtjournal.com/EN/10.12096/j.2096-4528.pgt.19035wind power predictiondata noise reductiondata set processing
spellingShingle Xincheng JIN
Xiuyuan YANG
Data Pre-processing Method Based on Distorted Data Noise Reduction and Its Application in Wind Power Prediction
发电技术
wind power prediction
data noise reduction
data set processing
title Data Pre-processing Method Based on Distorted Data Noise Reduction and Its Application in Wind Power Prediction
title_full Data Pre-processing Method Based on Distorted Data Noise Reduction and Its Application in Wind Power Prediction
title_fullStr Data Pre-processing Method Based on Distorted Data Noise Reduction and Its Application in Wind Power Prediction
title_full_unstemmed Data Pre-processing Method Based on Distorted Data Noise Reduction and Its Application in Wind Power Prediction
title_short Data Pre-processing Method Based on Distorted Data Noise Reduction and Its Application in Wind Power Prediction
title_sort data pre processing method based on distorted data noise reduction and its application in wind power prediction
topic wind power prediction
data noise reduction
data set processing
url https://www.pgtjournal.com/EN/10.12096/j.2096-4528.pgt.19035
work_keys_str_mv AT xinchengjin datapreprocessingmethodbasedondistorteddatanoisereductionanditsapplicationinwindpowerprediction
AT xiuyuanyang datapreprocessingmethodbasedondistorteddatanoisereductionanditsapplicationinwindpowerprediction