摘要
Boosted by a strong solar power market,the electricity grid is exposed to risk under an increasing share of fluctuant solar power.To increase the stability of the electricity grid,an accurate solar power forecast is needed to evaluate such fluctuations.In terms of forecast,solar irradiance is the key factor of solar power generation,which is affected by atmospheric conditions,including surface meteorological variables and column integrated variables.These variables involve multiple numerical timeseries and images.However,few studies have focused on the processing method of multiple data types in an interhour direct normal irradiance(DNI)forecast.In this study,a framework for predicting the DNI for a 10-min time horizon was developed,which included the nondimensionalization of multiple data types and time-series,development of a forecast model,and transformation of the outputs.Several atmospheric variables were considered in the forecast framework,including the historical DNI,wind speed and direction,relative humidity time-series,and ground-based cloud images.Experiments were conducted to evaluate the performance of the forecast framework.The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41%and a normalized root mean square error(n RMSE)of20.53%,and outperforms the persistent model with an improvement of 34%in the nRMSE.
Boosted by a strong solar power market, the electricity grid is exposed to risk under an increasing share of fluctuant solar power. To increase the stability of the electricity grid, an accurate solar power forecast is needed to evaluate such fluctuations. In terms of forecast, solar irradiance is the key factor of solar power generation,which is affected by atmospheric conditions, including surface meteorological variables and column integrated variables. These variables involve multiple numerical timeseries and images. However, few studies have focused on the processing method of multiple data types in an interhour direct normal irradiance(DNI) forecast. In this study,a framework for predicting the DNI for a 10-min time horizon was developed, which included the nondimensionalization of multiple data types and time-series,development of a forecast model, and transformation of the outputs. Several atmospheric variables were considered in the forecast framework, including the historical DNI, wind speed and direction, relative humidity time-series, and ground-based cloud images. Experiments were conducted to evaluate the performance of the forecast framework. The experimental results demonstrate that the proposed method performs well with a normalized mean bias error of 0.41%and a normalized root mean square error(n RMSE) of20.53%, and outperforms the persistent model with an improvement of 34% in the nRMSE.
基金
supported by the National Key Research and Development Program of China(No.2018YFB1500803)
National Natural Science Foundation of China(No.61773118,No.61703100)
Fundamental Research Funds for Central Universities.
作者简介
Tingting ZHU is currently working toward the Ph.D.degree in pattern recognition and artificial intelligence at School of Automation,in Southeast University,Nanjing,China.She achieved a fellowship jointly awarded by the Fonds de Recherche du Que-bec—Nature et Technologies(FRQNT)and the China Scholarship Council and studied as a visiting student at department of Atmospheric and Oceanic Sciences,in McGill University,Canada,from 2017 to 2018.Her current research interests include solar power generation forecast,machine learning and climate feedback.tingting_zhu2018@163.com;Hai ZHOU is a master,senior engineer and the director of Electric Meteorological Simulation and Applied Research Department,New Energy Research Center,China Electric Power Research Institute,and a backbone member of the scientific and technological research team on new energy power generation,power forecast and optimal dispatching in State Grid Corporation of China,mainly engaged in warning of meteorological disasters,monitoring and evaluation of meteorological resources in power grids.His research interests include numerical weather prediction,wind power,and photovoltaic power forecast.zhouhai@epri.sgcc.com.cn;Haikun WEI received the B.S.degree from the Department of Automation,North China University of Technology,China,in 1994,and the M.S.and Ph.D.degrees from the Research Institute of Automation,Southeast University,China,in 1997 and 2000,respectively.He was a Visiting Scholar with the RIKEN Brain Science Institute,Japan,from 2005 to 2007.He is currently a Professor with the School of Automation,Southeast University.His research interests include machine learning and renewable energy application.hkwei@seu.edu.cn;Xin ZHAO received the B.S.degree and the M.S.degree from Jiangnan University,Wuxi,China,in 2011 and 2014.She is currently working toward the Ph.D.degree in control science and engineering in the Southeast University,Nanjing,China.Her current research interests include machine learning and renewable energy power generation forecast.zhaoxin0504@163.com;Kanjian ZHANG received the B.S.degree in mathematics from Nankai University,China,in 1994,and the M.S.and Ph.D.degrees in control theory and control engineering from Southeast University,China,in 1997 and 2000,respectively.He is currently a Professor with the School of Automation,Southeast University.His research interests include nonlinear control theory and its applications,especially robust output feedback design and optimization control.kjzhang@seu.edu.cn;Jinxia ZHANG received the B.S.and Ph.D.degrees from the Department of Computer Science and Engineering,Nanjing University of Science and Technology,China,in 2009 and 2015,respectively.She was a Visiting Scholar with the Visual Attention Lab,Brigham and Women’s Hospital,and also with the Harvard Medical School from 2012 to 2014 in the USA.She is currently a Lecturer with the School of Automation,Southeast University.Her research interests include visual attention,visual saliency detection,computer vision and machine learning.jxzhang@seu.edu.cn
