潘可达.表后系统光伏分解与激励型需求响应基线负荷研究[D].广东工业大学,2022(01).

李康平.高渗透率分布式光伏下需求响应基线负荷估计方法研究[D].华北电力大学(北京),2020(06).

高漪,周瑜,张安龙,郭乐欣,陈金富.整县光伏下基于个性化联邦学习的光伏出力及负荷功率预测[J].电网技术,2023(11).

孔祥玉,马玉莹,艾芊,张曦元,李闯,肖白.新型电力系统多元用户的用电特征建模与用电负荷预测综述[J].电力系统自动化,2023(13).

李懿鑫,李正烁,刘聪聪.考虑光伏与电价不确定性的综合能源生产单元自调度报价策略研究[J].电网技术,2023(07).

张童彦,廖清芬,唐飞,李宇,王嘉乐,邓晖鹏.基于气象资源插值与迁移学习的广域分布式光伏功率预测方法[J].中国电机工程学报,2023(20).

陈娟,高江梅.基于演化博弈的户用光伏整县开发协同机制及仿真[J].电网技术,2023(02).

孙秋野,刘月,胡旌伟,胡旭光.基于GAN的非侵入式自能源建模[J].中国电机工程学报,2020(21).

龚莺飞,鲁宗相,乔颖,王强.光伏功率预测技术[J].电力系统自动化,2016(04).

刘宇,高山,杨胜春,姚建国.电力系统概率潮流算法综述[J].电力系统自动化,2014(23).

张钦,王锡凡,王建学,冯长有,刘林.电力市场下需求响应研究综述[J].电力系统自动化,2008(03).

Wu YuanKang,Lai YiHui,Huang ChengLiang,Phuong Nguyen Thi Bich,Tan WenShan.Artificial Intelligence Applications in Estimating Invisible Solar Power Generation[J].Energies,2022(4).

Li Kangping,Yan Jichuan,Hu Lin,Wang Fei,Zhang Ning.Two-Stage Decoupled Estimation Approach of Aggregated Baseline Load Under High Penetration of Behind-the-Meter PV System[J].IEEE TRANSACTIONS ON SMART GRID,2021.

Bu Fankun,Dehghanpour Kaveh,Yuan Yuxuan,Wang Zhaoyu,Guo Yifei.Disaggregating Customer-Level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars[J].IEEE TRANSACTIONS ON POWER SYSTEMS,2021.

Li Wenting,Yi Ming,Wang Meng,Wang Yishen,Shi Di,Wang Zhiwei.Real-Time Energy Disaggregation at Substations With Behind-the-Meter Solar Generation[J].IEEE TRANSACTIONS ON POWER SYSTEMS,2021.

Brown Joe,Abate Alessandro,Rogers Alex.Disaggregation of household solar energy generation using censored smart meter data[J].Energy & Buildings,2021.

Waswa Lewis,Chihota Munyaradzi Justice,Bekker Bernard.A Probabilistic Estimation of PV Capacity in Distribution Networks From Aggregated Net-Load Data[J].IEEE ACCESS,2021.

Khodayar Mahdi,Liu Guangyi,Wang Jianhui,Kaynak Okyay,Khodayar Mohammad E.Spatiotemporal Behind-the-Meter Load and PV Power Forecasting via Deep Graph Dictionary Learning.[J].IEEE transactions on neural networks and learning systems,2020.

Xuan Zhiming,Gao Xue,Li Kangping,Wang Fei,Ge Xinxin,Hou Yunhe.PV-Load Decoupling Based Demand Response Baseline Load Estimation Approach for Residential Customer With Distributed PV System[J].IEEE TRANSACTIONS ON INDUSTRY APPLICATIONS,2020.

Daniel L. Donaldson,Dilan Jayaweera.Effective solar prosumer identification using net smart meter data[J].International Journal of Electrical Power and Energy Systems,2020.

Karl Mason,Matthew J. Reno,Logan Blakely,Sadegh Vejdan,Santiago Grijalva.A deep neural network approach for behind-the-meter residential PV size, tilt and azimuth estimation[J].Solar Energy,2020.

Bu Fankun,Dehghanpour Kaveh,Yuan Yuxuan,Wang Zhaoyu,Zhang Yingchen.A Data-Driven Game-Theoretic Approach for Behind-the-Meter PV Generation Disaggregation[J].IEEE Transactions on Power Systems,2020.

Sun Mingyang,Zhang Tingqi,Wang Yi,Strbac Goran,Kang Chongqing.Using Bayesian Deep Learning to Capture Uncertainty for Residential Net Load Forecasting[J].IEEE Transactions on Power Systems,2020.

Carla Goncalves,Pierre Pinson,Ricardo Jorge Bessa.Towards Data Markets in Renewable Energy Forecasting[J].IEEE Transactions on Sustainable Energy,2020.

Kangping Li,Fei Wang,Zengqiang Mi,Mahmoud Fotuhi-Firuzabad,Neven Duić,Tieqiang Wang.Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation[J].Applied Energy,2019.

Sossan Fabrizio,Nespoli Lorenzo,Medici Vasco,Paolone Mario.Unsupervised Disaggregation of Photovoltaic Production From Composite Power Flow Measurements of Heterogeneous Prosumers[J].IEEE Transactions on Industrial Informatics,2018.

Wang Yi,Zhang Ning,Chen Qixin,Kirschen Daniel S.,Li Pan,Xia Qing.Data-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV[J].IEEE Transactions on Power Systems,2018.

Grid Integration Systems and Mobility Group, SLAC National Accelerator Laboratory, 2575 Sand Hill Rd, Menlo Park, CA 94025, USA,Energy Storage and Distributed Resources Division, Lawrence Berkeley National Laboratory, Berkeley, CA, USA,Civil and Environmental Engineering, Stanford University, Stanford, CA, USA,Riverside Public Utilities, Riverside, CA, USA,Energy and Resources Group, University of California, Berkeley, CA, USA,Infrastructure Systems, Cyber and Physical Resilience, Lawrence Livermore National Laboratory, USA.Disaggregating solar generation from feeder-level measurements[J].Sustainable Energy, Grids and Networks,2018.

William F. Holmgren,Clifford W. Hansen,Mark Mikofski.pvlib python: a python package for modeling solar energy systems.[J].J. Open Source Software,2018.

Marco Pierro,Matteo De Felice,Enrico Maggioni,David Moser,Alessandro Perotto,Francesco Spada,Cristina Cornaro.Data-driven upscaling methods for regional photovoltaic power estimation and forecast using satellite and numerical weather prediction data[J].Solar Energy,2017.

Kari Lappalainen,Seppo Valkealahti.Output power variation of different PV array configurations during irradiance transitions caused by moving clouds[J].Applied Energy,2017.

Xiaochen Zhang,Santiago Grijalva.A Data-Driven Approach for Detection and Estimation of Residential PV Installations.[J].IEEE Trans. Smart Grid,2016.

Shaker, Hamid,Zareipour, Hamidreza,Wood, David.A Data-Driven Approach for Estimating the Power Generation of Invisible Solar Sites[J].IEEE transactions on smart grid,2016.

Shaker, Hamid,Zareipour, Hamidreza,Wood, David.Estimating Power Generation of Invisible Solar Sites Using Publicly Available Data[J].IEEE transactions on smart grid,2016.