Functional clustering analysis of Chinese provincial wind power generation

China is a broad territory country. There are significant differences in the terrain, climate, and other environmental factors between different provinces, which affect wind power generation. In order to better analyze the situation of wind power generation in Chinese provinces, this paper uses the...

Full description

Bibliographic Details
Main Authors: Yizheng Fu, Zhifang Su
Format: Article
Language:English
Published: SAGE Publishing 2021-03-01
Series:Energy Exploration & Exploitation
Online Access:https://doi.org/10.1177/0144598720909170
id doaj-956073d64c6f40a29b4792bf9e12bd10
record_format Article
spelling doaj-956073d64c6f40a29b4792bf9e12bd102021-03-05T00:33:39ZengSAGE PublishingEnergy Exploration & Exploitation0144-59872048-40542021-03-013910.1177/0144598720909170Functional clustering analysis of Chinese provincial wind power generationYizheng FuZhifang SuChina is a broad territory country. There are significant differences in the terrain, climate, and other environmental factors between different provinces, which affect wind power generation. In order to better analyze the situation of wind power generation in Chinese provinces, this paper uses the functional clustering analysis to classify the monthly data of wind power generation in 30 Chinese provinces from March 2013 to October 2019. The empirical results of this paper show that the wind energy generation in Chinese provinces can be divided into three categories, and the results are consistent with the actual situation. In this paper, functional clustering analysis is used to analyze monthly data, compared with the traditional clustering analysis to analyze annual data which are obtained by accumulated monthly data. Higher-dimensional data can be used for analysis to reduce information loss. Moreover, data can be viewed as functions, and more information can be mined by analyzing derivative functions, and so on. The analysis of wind energy generation has certain guiding significance for the development and utilization of renewable energy.https://doi.org/10.1177/0144598720909170
collection DOAJ
language English
format Article
sources DOAJ
author Yizheng Fu
Zhifang Su
spellingShingle Yizheng Fu
Zhifang Su
Functional clustering analysis of Chinese provincial wind power generation
Energy Exploration & Exploitation
author_facet Yizheng Fu
Zhifang Su
author_sort Yizheng Fu
title Functional clustering analysis of Chinese provincial wind power generation
title_short Functional clustering analysis of Chinese provincial wind power generation
title_full Functional clustering analysis of Chinese provincial wind power generation
title_fullStr Functional clustering analysis of Chinese provincial wind power generation
title_full_unstemmed Functional clustering analysis of Chinese provincial wind power generation
title_sort functional clustering analysis of chinese provincial wind power generation
publisher SAGE Publishing
series Energy Exploration & Exploitation
issn 0144-5987
2048-4054
publishDate 2021-03-01
description China is a broad territory country. There are significant differences in the terrain, climate, and other environmental factors between different provinces, which affect wind power generation. In order to better analyze the situation of wind power generation in Chinese provinces, this paper uses the functional clustering analysis to classify the monthly data of wind power generation in 30 Chinese provinces from March 2013 to October 2019. The empirical results of this paper show that the wind energy generation in Chinese provinces can be divided into three categories, and the results are consistent with the actual situation. In this paper, functional clustering analysis is used to analyze monthly data, compared with the traditional clustering analysis to analyze annual data which are obtained by accumulated monthly data. Higher-dimensional data can be used for analysis to reduce information loss. Moreover, data can be viewed as functions, and more information can be mined by analyzing derivative functions, and so on. The analysis of wind energy generation has certain guiding significance for the development and utilization of renewable energy.
url https://doi.org/10.1177/0144598720909170
work_keys_str_mv AT yizhengfu functionalclusteringanalysisofchineseprovincialwindpowergeneration
AT zhifangsu functionalclusteringanalysisofchineseprovincialwindpowergeneration
_version_ 1724231271866957824