Prediction of photovoltaic power output based on similar day analysis, genetic algorithm and extreme learning machine
暂无分享,去创建一个
Nanrun Zhou | Minlin Jiang | Nanrun Zhou | L. Gong | Minlin Jiang | Yi Zhou | Lihua Gong | Yi Zhou
[1] Arno Krenzinger,et al. Degradation analysis of a photovoltaic generator after operating for 15 years in southern Brazil , 2020 .
[2] C. Balakrishna Moorthy,et al. APPLICATION OF GENETIC ALGORITHM TO NEURAL NETWORK MODEL FOR ESTIMATION OF WIND POWER POTENTIAL , 2010 .
[3] Da Liu,et al. Random forest solar power forecast based on classification optimization , 2019, Energy.
[4] Saifur Rahman,et al. Hour-ahead solar PV power forecasting using SVR based approach , 2017, 2017 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).
[5] Igor Mladenovic,et al. Economic growth forecasting by artificial neural network with extreme learning machine based on trade, import and export parameters , 2016, Comput. Hum. Behav..
[6] Jianzhou Wang,et al. Research and application of a hybrid forecasting framework based on multi-objective optimization for electrical power system , 2018 .
[7] Yacine Rezgui,et al. Tree-based ensemble methods for predicting PV power generation and their comparison with support vector regression , 2018, Energy.
[8] Khubaib Amjad Alam,et al. Support vector regression based prediction of global solar radiation on a horizontal surface , 2015 .
[9] Francisco J. Batlles,et al. Solar irradiance forecasting at one-minute intervals for different sky conditions using sky camera images , 2015 .
[10] Soteris A. Kalogirou,et al. Machine learning methods for solar radiation forecasting: A review , 2017 .
[11] Andreas Svensson,et al. Probabilistic forecasting of electricity consumption, photovoltaic power generation and net demand of an individual building using Gaussian Processes , 2018 .
[12] Na Dong,et al. A novel convolutional neural network framework based solar irradiance prediction method , 2020 .
[13] Lijun Wu,et al. Short-term power prediction for photovoltaic power plants using a hybrid improved Kmeans-GRA-Elman model based on multivariate meteorological factors and historical power datasets , 2018, Energy Conversion and Management.
[14] Kok Soon Tey,et al. Forecasting of photovoltaic power generation and model optimization: A review , 2018 .
[15] Paulo Canhoto,et al. Short-term forecasts of GHI and DNI for solar energy systems operation: assessment of the ECMWF integrated forecasting system in southern Portugal , 2018, Solar Energy.
[16] S. Miller,et al. Short-term solar irradiance forecasting via satellite/model coupling , 2017, Solar Energy.
[17] Aoran Xu,et al. A new PV generation power prediction model based on GA-BP neural network with artificial classification of history day , 2018, 2018 International Conference on Power System Technology (POWERCON).
[18] Shahaboddin Shamshirband,et al. Extreme learning machine for prediction of heat load in district heating systems , 2016 .
[19] Mehdi Seyedmahmoudian,et al. Short-term PV power forecasting using hybrid GASVM technique , 2019, Renewable Energy.
[20] Yan Su,et al. Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines , 2016 .
[21] Shahaboddin Shamshirband,et al. Predicting the wind power density based upon extreme learning machine , 2015 .
[22] M. G. De Giorgi,et al. Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine , 2016 .
[23] L. D. Monache,et al. An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting , 2016 .
[24] Yue Zhang,et al. Day-Ahead Power Output Forecasting for Small-Scale Solar Photovoltaic Electricity Generators , 2015, IEEE Transactions on Smart Grid.
[25] Shervin Motamedi,et al. Extreme learning machine based prediction of daily dew point temperature , 2015, Comput. Electron. Agric..
[26] Mohamed Benbouzid,et al. Economical Evaluation and Optimal Energy Management of a Stand-Alone Hybrid Energy System Handling in Genetic Algorithm Strategies , 2018, Electronics.
[27] Joao P. S. Catalao,et al. Compressive Spatio-Temporal Forecasting of Meteorological Quantities and Photovoltaic Power , 2016, IEEE Transactions on Sustainable Energy.
[28] Yingzhong Gu,et al. Markov Chain model for solar farm generation and its application to generation performance evaluation , 2018, Journal of Cleaner Production.
[29] B D Stollar,et al. Bacterial expression of anti-DNA antibody domains. , 1997, Methods.
[30] Ming-Lang Tseng,et al. Renewable energy prediction: A novel short-term prediction model of photovoltaic output power , 2019, Journal of Cleaner Production.
[31] Di Chen,et al. Entropy method combined with extreme learning machine method for the short-term photovoltaic power generation forecasting , 2016 .
[32] Chi Zhang,et al. Day-ahead spatiotemporal solar irradiation forecasting using frequency-based hybrid principal component analysis and neural network , 2019, Applied Energy.
[33] Jian Xiao,et al. Short-Term Solar Power Forecasting Based on Weighted Gaussian Process Regression , 2018, IEEE Transactions on Industrial Electronics.
[34] Irena Koprinska,et al. Univariate and multivariate methods for very short-term solar photovoltaic power forecasting , 2016 .
[35] Soteris A. Kalogirou,et al. Artificial neural networks in renewable energy systems applications: a review , 2001 .
[36] Chunxiang Yang,et al. An Improved Photovoltaic Power Forecasting Model With the Assistance of Aerosol Index Data , 2015, IEEE Transactions on Sustainable Energy.
[37] Manoja Kumar Behera,et al. A comparative study on short-term PV power forecasting using decomposition based optimized extreme learning machine algorithm , 2020 .
[38] Shengxian Zhuang,et al. An ensemble prediction intervals approach for short-term PV power forecasting , 2017 .
[39] Chee Kheong Siew,et al. Extreme learning machine: Theory and applications , 2006, Neurocomputing.
[40] Haiyang Lin,et al. The energy-saving potential of an office under different pricing mechanisms – Application of an agent-based model , 2017 .
[41] Yanbo Che,et al. A short-term photovoltaic power prediction model based on the gradient boost decision tree , 2018 .
[42] Qie Sun,et al. Outline of principles for building scenarios – Transition toward more sustainable energy systems , 2017 .
[43] Hai Zhou,et al. A PV power interval forecasting based on seasonal model and nonparametric estimation algorithm , 2019, Solar Energy.
[44] Guang-Bin Huang,et al. Extreme learning machine: a new learning scheme of feedforward neural networks , 2004, 2004 IEEE International Joint Conference on Neural Networks (IEEE Cat. No.04CH37541).
[45] Robin Girard,et al. Short-Term Spatio-Temporal Forecasting of Photovoltaic Power Production , 2018, IEEE Transactions on Sustainable Energy.
[46] M. Alia-Martinez,et al. Smart baseline models for solar irradiation forecasting , 2016 .
[47] Qie Sun,et al. Power Generation Efficiency and Prospects of Floating Photovoltaic Systems , 2017 .