Wavelet Decomposition and Convolutional LSTM Networks Based Improved Deep Learning Model for Solar Irradiance Forecasting
暂无分享,去创建一个
Jie Li | Zhao Zhen | Kangping Li | Zhanyao Zhang | Yili Yu | Fei Wang | Jie Li | Fei Wang | Zhanyao Zhang | Yili Yu | Z. Zhen | Kangping Li
[1] Zheng Wang,et al. Multi-level wavelet decomposition based day-ahead solar irradiance forecasting , 2018, 2018 IEEE Power & Energy Society Innovative Smart Grid Technologies Conference (ISGT).
[2] Jun-Geol Baek,et al. A Novel Image Feature for the Remaining Useful Lifetime Prediction of Bearings Based on Continuous Wavelet Transform and Convolutional Neural Network , 2018, Applied Sciences.
[3] Ioannis P. Panapakidis,et al. Day-ahead natural gas demand forecasting based on the combination of wavelet transform and ANFIS/genetic algorithm/neural network model , 2017 .
[4] Joao P. S. Catalao,et al. Time-Section Fusion Pattern Classification Based Day-Ahead Solar Irradiance Ensemble Forecasting Model Using Mutual Iterative Optimization , 2018 .
[5] Fei Wang,et al. Synchronous Pattern Matching Principle-Based Residential Demand Response Baseline Estimation: Mechanism Analysis and Approach Description , 2018, IEEE Transactions on Smart Grid.
[6] Carlos F.M. Coimbra,et al. History and trends in solar irradiance and PV power forecasting: A preliminary assessment and review using text mining , 2018, Solar Energy.
[7] Nadejda Komendantova,et al. Renewables 2012 Global Status Report , 2012 .
[8] Glenn Platt,et al. Machine learning for solar irradiance forecasting of photovoltaic system , 2016 .
[9] Henrik Madsen,et al. Online short-term solar power forecasting , 2009 .
[10] Geoffrey E. Hinton,et al. Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.
[11] M. David,et al. Solar irradiation forecasting: state-of-the-art and proposition for future developments for small-scale insular grids , 2012 .
[12] J. Catalão,et al. Image phase shift invariance based cloud motion displacement vector calculation method for ultra-short-term solar PV power forecasting , 2018 .
[13] Aitor Almeida,et al. Predicting Human Behaviour with Recurrent Neural Networks , 2018 .
[14] JØrgen Christensen-Dalsgaard. Physics of solar-like oscillations , 2004 .
[15] Fei Wang,et al. Comparative Study on KNN and SVM Based Weather Classification Models for Day Ahead Short Term Solar PV Power Forecasting , 2017 .
[16] Martin Hirzel,et al. Machine learning in Python with no strings attached , 2019, MAPL@PLDI.
[17] Gordon Reikard. Predicting solar radiation at high resolutions: A comparison of time series forecasts , 2009 .
[18] J. A. Ruiz-Arias,et al. Short-term solar radiation forecasting by advecting and diffusing MSG cloud index , 2017 .
[19] Guang Yang,et al. Solar irradiance feature extraction and support vector machines based weather status pattern recognition model for short-term photovoltaic power forecasting , 2015 .
[20] Tomonobu Senjyu,et al. Optimizing Re-planning Operation for Smart House Applying Solar Radiation Forecasting , 2014 .
[21] Jürgen Schmidhuber,et al. Long Short-Term Memory , 1997, Neural Computation.
[22] C. Coimbra,et al. Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database , 2011 .
[23] Fei Wang,et al. Multi-objective optimization model of source-load-storage synergetic dispatch for building energy system based on TOU price demand response , 2017, 2017 IEEE Industry Applications Society Annual Meeting.
[24] Ting Liu,et al. Recent advances in convolutional neural networks , 2015, Pattern Recognit..
[25] Fei Wang,et al. Short-Term Solar Irradiance Forecasting Model Based on Artificial Neural Network Using Statistical Feature Parameters , 2012 .
[26] R. Verzijlbergh,et al. Improved model output statistics of numerical weather prediction based irradiance forecasts for solar power applications , 2015 .
[27] Amy Loutfi,et al. A review of unsupervised feature learning and deep learning for time-series modeling , 2014, Pattern Recognit. Lett..
[28] Yoshua Bengio,et al. Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.
[29] Fatih Onur Hocaoglu,et al. Novel short term solar irradiance forecasting models , 2018, Renewable Energy.
[30] Hanchen Xu,et al. The values of market-based demand response on improving power system reliability under extreme circumstances , 2017 .
[31] Giorgio Graditi,et al. Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production , 2017 .
[32] Bernhard Sick,et al. Deep Learning for solar power forecasting — An approach using AutoEncoder and LSTM Neural Networks , 2016, 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC).
[33] Fei Wang,et al. Correlation feature selection and mutual information theory based quantitative research on meteorological impact factors of module temperature for solar photovoltaic systems , 2016 .
[34] Roberto Medina,et al. Classification of Architectural Heritage Images Using Deep Learning Techniques , 2017 .
[35] José R. Dorronsoro,et al. Hybrid machine learning forecasting of solar radiation values , 2016, Neurocomputing.
[36] G. Gordillo,et al. Statistical modeling for global solar radiation forecasting in Bogotá , 2010, 2010 35th IEEE Photovoltaic Specialists Conference.
[37] Amandeep Sharma,et al. Forecasting daily global solar irradiance generation using machine learning , 2018 .
[38] Fei Wang,et al. Search Improvement Process-Chaotic Optimization-Particle Swarm Optimization-Elite Retention Strategy and Improved Combined Cooling-Heating-Power Strategy Based Two-Time Scale Multi-Objective Optimization Model for Stand-Alone Microgrid Operation , 2017 .
[39] Wei Qiao,et al. Short-term solar power prediction using a support vector machine , 2013 .
[40] Stéphane Mallat,et al. A Theory for Multiresolution Signal Decomposition: The Wavelet Representation , 1989, IEEE Trans. Pattern Anal. Mach. Intell..
[41] R. Inman,et al. Solar forecasting methods for renewable energy integration , 2013 .
[42] Fei Wang,et al. Multi-Objective Optimization Model of Source–Load–Storage Synergetic Dispatch for a Building Energy Management System Based on TOU Price Demand Response , 2018, IEEE Transactions on Industry Applications.
[43] A. Hammer,et al. Short term forecasting of solar radiation based on satellite data , 2004 .
[44] Fei Wang,et al. Dynamic Price Vector Formation Model-Based Automatic Demand Response Strategy for PV-Assisted EV Charging Stations , 2017, IEEE Transactions on Smart Grid.
[45] Reinaldo Tonkoski,et al. Using Markov Switching Model for solar irradiance forecasting in remote microgrids , 2016, 2016 IEEE Energy Conversion Congress and Exposition (ECCE).
[46] Zechun Hu,et al. Photovoltaic and solar power forecasting for smart grid energy management , 2015 .
[47] Fei Wang,et al. A Distributed PV System Capacity Estimation Approach Based on Support Vector Machine with Customer Net Load Curve Features , 2018 .
[48] M. Diagne,et al. Review of solar irradiance forecasting methods and a proposition for small-scale insular grids , 2013 .
[49] Yanbo Che,et al. A short-term photovoltaic power prediction model based on the gradient boost decision tree , 2018 .
[50] Chao-Ming Huang,et al. A Weather-Based Hybrid Method for 1-Day Ahead Hourly Forecasting of PV Power Output , 2014, IEEE Transactions on Sustainable Energy.
[51] Sajid Hussain,et al. Day ahead hourly forecast of solar irradiance for Abu Dhabi, UAE , 2016, 2016 IEEE Smart Energy Grid Engineering (SEGE).
[52] Holger R. Maier,et al. Neural networks for the prediction and forecasting of water resource variables: a review of modelling issues and applications , 2000, Environ. Model. Softw..
[53] Na Li,et al. Solar generation prediction using the ARMA model in a laboratory-level micro-grid , 2012, 2012 IEEE Third International Conference on Smart Grid Communications (SmartGridComm).
[54] Yugang Niu,et al. Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM , 2018 .
[55] Consolación Gil,et al. Optimization methods applied to renewable and sustainable energy: A review , 2011 .
[56] Ruqiang Yan,et al. Learning to Monitor Machine Health with Convolutional Bi-Directional LSTM Networks , 2017, Sensors.