黑龙江省光伏气候的主成分与聚类分析

Zhiwen Wang; Dazhi Yang; Bai Liu; Haizhi Qiu; Zhuhang Shao

doi:10.19912/j.0254-0096.tynxb.2025-0334

黑龙江省光伏气候的主成分与聚类分析

Translated title of the contribution: PRINCIPAL COMPONENT AND CLUSTER ANALYSIS OF PHOTOVOLTAIC CLIMATE IN HEILONGJIANG PROVINCE

Zhiwen Wang
, Dazhi Yang
, Bai Liu^*
, Haizhi Qiu
, Zhuhang Shao

^*Corresponding author for this work

School of Electrical Engineering and Automation, Harbin Institute of Technology
Fuyu County Meteorological Bureau

Research output: Contribution to journal › Article › peer-review

Abstract

This study proposes a PV resource rating method based on principal component analysis（PCA）and clustering algorithms. The methodology involves acquiring solar irradiance and auxiliary meteorological variables for Heilongjiang Province from 2016 to 2020，derived from Himawari-8 satellite data with a spatial resolution of 4 km. Subsequently，nine temporal features are extracted for the five variables using time series analysis，and PCA is applied to reduce the dimensionality of the extracted features. Finally，a spatial distribution map of the PV climate at a power plant scale for Heilongjiang Province is generated through clustering analysis. This map categorizes the total solar resource of Heilongjiang Province into three levels：general，relatively abundant，and very abundant，providing significant scientific support for subsequent refined PV resource assessments.

Translated title of the contribution	PRINCIPAL COMPONENT AND CLUSTER ANALYSIS OF PHOTOVOLTAIC CLIMATE IN HEILONGJIANG PROVINCE
Original language	Chinese (Traditional)
Pages (from-to)	564-570
Number of pages	7
Journal	Taiyangneng Xuebao/Acta Energiae Solaris Sinica
Volume	47
Issue number	7
DOIs	https://doi.org/10.19912/j.0254-0096.tynxb.2025-0334
State	Published - Jul 2026
Externally published	Yes

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

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10.19912/j.0254-0096.tynxb.2025-0334

Cite this

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title = "黑龙江省光伏气候的主成分与聚类分析",

abstract = "This study proposes a PV resource rating method based on principal component analysis（PCA）and clustering algorithms. The methodology involves acquiring solar irradiance and auxiliary meteorological variables for Heilongjiang Province from 2016 to 2020，derived from Himawari-8 satellite data with a spatial resolution of 4 km. Subsequently，nine temporal features are extracted for the five variables using time series analysis，and PCA is applied to reduce the dimensionality of the extracted features. Finally，a spatial distribution map of the PV climate at a power plant scale for Heilongjiang Province is generated through clustering analysis. This map categorizes the total solar resource of Heilongjiang Province into three levels：general，relatively abundant，and very abundant，providing significant scientific support for subsequent refined PV resource assessments.",

keywords = "Heilongjiang province, clustering algorithms, photovoltaic, principal component analysis, resource assessment, solar energy",

author = "Zhiwen Wang and Dazhi Yang and Bai Liu and Haizhi Qiu and Zhuhang Shao",

year = "2026",

month = jul,

doi = "10.19912/j.0254-0096.tynxb.2025-0334",

language = "繁体中文",

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T1 - 黑龙江省光伏气候的主成分与聚类分析

AU - Wang, Zhiwen

AU - Yang, Dazhi

AU - Liu, Bai

AU - Qiu, Haizhi

AU - Shao, Zhuhang

PY - 2026/7

Y1 - 2026/7

N2 - This study proposes a PV resource rating method based on principal component analysis（PCA）and clustering algorithms. The methodology involves acquiring solar irradiance and auxiliary meteorological variables for Heilongjiang Province from 2016 to 2020，derived from Himawari-8 satellite data with a spatial resolution of 4 km. Subsequently，nine temporal features are extracted for the five variables using time series analysis，and PCA is applied to reduce the dimensionality of the extracted features. Finally，a spatial distribution map of the PV climate at a power plant scale for Heilongjiang Province is generated through clustering analysis. This map categorizes the total solar resource of Heilongjiang Province into three levels：general，relatively abundant，and very abundant，providing significant scientific support for subsequent refined PV resource assessments.

AB - This study proposes a PV resource rating method based on principal component analysis（PCA）and clustering algorithms. The methodology involves acquiring solar irradiance and auxiliary meteorological variables for Heilongjiang Province from 2016 to 2020，derived from Himawari-8 satellite data with a spatial resolution of 4 km. Subsequently，nine temporal features are extracted for the five variables using time series analysis，and PCA is applied to reduce the dimensionality of the extracted features. Finally，a spatial distribution map of the PV climate at a power plant scale for Heilongjiang Province is generated through clustering analysis. This map categorizes the total solar resource of Heilongjiang Province into three levels：general，relatively abundant，and very abundant，providing significant scientific support for subsequent refined PV resource assessments.

KW - Heilongjiang province

KW - clustering algorithms

KW - photovoltaic

KW - principal component analysis

KW - resource assessment

KW - solar energy

UR - https://www.scopus.com/pages/publications/105046351346

U2 - 10.19912/j.0254-0096.tynxb.2025-0334

DO - 10.19912/j.0254-0096.tynxb.2025-0334

M3 - 文章

AN - SCOPUS:105046351346

SN - 0254-0096

VL - 47

SP - 564

EP - 570

JO - Taiyangneng Xuebao/Acta Energiae Solaris Sinica

JF - Taiyangneng Xuebao/Acta Energiae Solaris Sinica

IS - 7

ER -

黑龙江省光伏气候的主成分与聚类分析

Abstract

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