Ultra-Short-Term Forecast of Photovoltaic Output Power under Fog and Haze Weather

被引：20
|
作者：
Liu, Weiliang [1 ]
Liu, Changliang [1 ]
Lin, Yongjun [1 ]
Ma, Liangyu [1 ]
Xiong, Feng [1 ]
Li, Jintuo [1 ]
机构：
[1] North China Elect Power Univ, State Key Lab Alternate Elect Power Syst Renewabl, Baoding 071003, Peoples R China
来源：
ENERGIES | 2018年 / 11卷 / 03期
基金：
北京市自然科学基金;
关键词：
fog and haze; photovoltaic output power; forecast; aerosol optical depth; particle matter concentration; machine learning; efficiency reduction; AEROSOL OPTICAL-THICKNESS; IRRADIANCE PREDICTIONS; SKY IRRADIANCE; PERFORMANCE; DUST; TRANSMITTANCE; VALIDATION; PARAMETERS; REST2; PM2.5;
D O I：
10.3390/en11030528
中图分类号：
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号：
0807 ; 0820 ;
摘要：
Fog and haze (F-H) weather has been occurring frequently in China since 2012, which affects the output power of photovoltaic (PV) generation dramatically by directly weakening solar irradiance and aggravating dust deposition on PV panels. The ultra-short-term forecast method presented in this study would help to fully reflect the dual effects of F-H on PV output power. Aiming at the weakening effect on solar irradiance, estimation models of atmospheric aerosol optical depth (AOD) based on particle matter (PM) concentration were established with machine learning (ML) method, and the total irradiance received by PV panels was calculated based on simplified REST2 model. Aiming at the aggravating effect on dust deposition on PV panels, sample set of "cumulative PM concentration-efficiency reduction" was constructed through special measurement experiments, then the efficiency reduction under certain dust deposition state was estimated with similar-day choosing method. Based on photoelectric conversion model, PM concentration prediction and weather forecast information, ultra-short-term forecast of PV output power was realized. Experimental results proved the validity and feasibility of the presented forecast method.
引用
收藏
页数：22
相关论文
共 50 条
  • [1] Adaptive masked network for ultra-short-term photovoltaic forecast
    Ma, Qiaoyu
    Fu, Xueqian
    Yang, Qiang
    Qiu, Dawei
    ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE, 2025, 139
  • [2] Ultra-Short-term Photovoltaic Output Power Forecasting using Deep Learning Algorithms
    Dimd, Berhane Darsene
    Voller, Steve
    Midtgard, Ole-Morten
    Zenebe, Tarikua Mekashaw
    2022 IEEE 21ST MEDITERRANEAN ELECTROTECHNICAL CONFERENCE (IEEE MELECON 2022), 2022, : 837 - 842
  • [3] Improved Random Forest Method for Ultra-short-term Prediction of the Output Power of a Photovoltaic Cluster
    Yang, Mao
    Zhao, Meng
    Liu, Dingze
    Ma, Miaomiao
    Su, Xin
    FRONTIERS IN ENERGY RESEARCH, 2021, 9
  • [4] Improved Random Forest Method for Ultra-short-term Prediction of the Output Power of a Photovoltaic Cluster
    Yang, Mao
    Zhao, Meng
    Liu, Dingze
    Ma, Miaomiao
    Su, Xin
    Frontiers in Energy Research, 2021, 9
  • [5] Predicting Ultra-Short-Term Wind Power Combinations Under Extreme Weather Conditions
    Li, Wanting
    Yang, Tongguang
    Yang, Jingyu
    Peng, Li
    IEEE ACCESS, 2025, 13 : 26575 - 26588
  • [6] Forecast for wind power at ultra-short-term based on a composite model
    Li, Chen
    Cao, Dong-Sheng
    Zhao, Zi-Teng
    Wang, Xuan
    Xie, Xi-Yang
    ENERGY REPORTS, 2024, 12 : 4076 - 4082
  • [7] Ultra-short-term power forecast of photovoltaic power station based on VMD-LSTM model optimised by SSA
    Yizhou, Jing
    Siqi, Yang
    Kegeng, Zhang
    APPLIED MATHEMATICS AND NONLINEAR SCIENCES, 2022, 8 (02) : 823 - 834
  • [8] Interval Prediction Algorithm for Ultra-short-term Photovoltaic Output and Its Application
    Li M.
    Lin X.
    Zhang Z.
    Weng H.
    Dianli Xitong Zidonghua/Automation of Electric Power Systems, 2019, 43 (03): : 10 - 16
  • [9] Ultra-short-term Prediction of Photovoltaic Power Based on Dataset Distillation
    Zheng K.
    Wang L.
    Hao Y.
    Wang B.
    Zhongguo Dianji Gongcheng Xuebao/Proceedings of the Chinese Society of Electrical Engineering, 2024, 44 (13): : 5196 - 5207
  • [10] Review of Ultra-short-term Forecasting Methods for Photovoltaic Power Generation
    Dong C.
    Wang Z.
    Bai J.
    Jiang J.
    Wang B.
    Liu G.
    Gaodianya Jishu/High Voltage Engineering, 2023, 49 (07): : 2938 - 2951
12345