Research graph
References from A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Local targets link to admitted publications; unresolved targets remain external evidence.
A review of deep learning for renewable energy forecasting
10.1016/j.enconman.2019.111799 · 2019 · External reference
A review and evaluation of the state-of-the-art in PV solar power forecasting: techniques and optimization
10.1016/j.rser.2020.109792 · 2020 · External reference
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
10.1016/j.scs.2020.102052 · 2020 · External reference
Deep learning methods and applications for electrical power systems: a comprehensive review
10.1002/er.5331 · 2020 · External reference
A survey of deep learning techniques: application in wind and solar energy resources
10.1109/access.2019.2951750 · 2019 · External reference
State of the art of machine learning models in energy systems, a systematic review
10.3390/en12071301 · 2019 · External reference
Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network
10.1016/j.enconman.2018.04.021 · 2018 · External reference
The future of forecasting for renewable energy
2020 · External reference
Deep learning an overview
2019 · External reference
Machine learning methods for solar radiation forecasting: a review
10.1016/j.renene.2016.12.095 · 2017 · External reference
Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques
10.1049/iet-rpg.2018.5649 · 2019 · External reference
Taxonomy research of artificial intelligence for deterministic solar power forecasting
10.1016/j.enconman.2020.112909 · 2020 · External reference
Advanced methods for photovoltaic output power forecasting: a review
10.3390/app10020487 · 2020 · External reference
Deterministic wind energy forecasting: a review of intelligent predictors and auxiliary methods
10.1016/j.enconman.2019.05.020 · 2019 · External reference
A survey of artificial neural network in wind energy systems
10.1016/j.apenergy.2018.07.084 · 2018 · External reference
Scene learning: deep convolutional networks for wind power prediction by embedding turbines into grid space
10.1016/j.apenergy.2019.01.010 · 2019 · External reference
An adaptive hybrid model for day-ahead photovoltaic output power prediction
10.1016/j.jclepro.2019.118858 · 2020 · External reference
Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network
10.1016/j.energy.2020.117693 · 2020 · External reference
Extreme gradient boosting and deep neural network based ensemble learning approach to forecast hourly solar irradiance
10.1016/j.jclepro.2020.123285 · 2021 · External reference
Gaussian mixture model coupled recurrent neural networks for wind speed interval forecast
10.1016/j.enconman.2019.06.083 · 2019 · External reference
Solar PV output prediction from video streams using convolutional neural networks
10.1039/c7ee03420b · 2018 · External reference
Deep learning based surface irradiance mapping model for solar PV power forecasting using sky image
2020 · External reference
Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network
10.1016/j.energy.2020.118441 · 2020 · External reference
Unresolved reference
External reference
Learning temporal and spatial correlations jointly: a unified framework for wind speed prediction
10.1109/tste.2019.2897136 · 2020 · External reference
Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model
10.1016/j.apenergy.2019.114259 · 2020 · External reference
Spatio-temporal graph deep neural network for short-term wind speed forecasting
10.1109/tste.2018.2844102 · 2018 · External reference
Convolutional graph autoencoder: a generative deep neural network for probabilistic spatio-temporal solar irradiance forecasting
10.1109/tste.2019.2897688 · 2020 · External reference
Deep Learning for solar power forecasting—An approach using AutoEncoder and LSTM Neural Networks
2016 · External reference
A model combining convolutional neural network and LightGBM algorithm for ultra-short-term wind power forecasting
10.1109/access.2019.2901920 · 2019 · External reference
Short-term wind power forecasting approach based on Seq2Seq model using NWP data
10.1016/j.energy.2020.118371 · 2020 · External reference
Hourly day-ahead solar irradiance prediction using weather forecasts by LSTM
10.1016/j.energy.2018.01.177 · 2018 · External reference
Solar irradiance forecasting using deep neural networks
10.1016/j.procs.2017.09.045 · 2017 · External reference
A novel deep learning ensemble model with data denoising for short-term wind speed forecasting
10.1016/j.enconman.2020.112524 · 2020 · External reference
Deep solar radiation forecasting with convolutional neural network and long short-term memory network algorithms
10.1016/j.apenergy.2019.113541 · 2019 · External reference
Forecasting solar power using long-short term memory and convolutional neural networks
10.1109/access.2018.2883330 · 2018 · External reference
Probabilistic short-term wind power forecasting based on deep neural networks
2016 · External reference
Short-term forecasting and uncertainty analysis of wind turbine power based on long short-term memory network and Gaussian mixture model
10.1016/j.apenergy.2019.03.044 · 2019 · External reference
A new data driven long-term solar yield analysis model of photovoltaic power plants
10.1109/access.2020.3011982 · 2020 · External reference
Time series forecasting of solar power generation for large-scale photovoltaic plants
10.1016/j.renene.2019.12.131 · 2020 · External reference
Generalized Hampel Filters
10.1186/s13634-016-0383-6 · 2016 · External reference
Application of functional deep belief network for estimating daily global solar radiation: a case study in China
10.1016/j.energy.2019.116502 · 2020 · External reference
Quadratic spline interpolation
10.1016/0021-9045(89)90045-2 · 1989 · External reference
Improving solar forecasting using Deep Learning and Portfolio Theory integration
10.1016/j.energy.2020.117016 · 2020 · External reference
Wind power prediction based on high-frequency SCADA data along with isolation forest and deep learning neural networks
10.1016/j.ijepes.2020.105835 · 2020 · External reference
Data processing strategies in wind energy forecasting models and applications: a comprehensive review
10.1016/j.apenergy.2019.04.188 · 2019 · External reference
Multi-objective data-ensemble wind speed forecasting model with stacked sparse autoencoder and adaptive decomposition-based error correction
10.1016/j.apenergy.2019.113686 · 2019 · External reference
Solar radiation prediction using recurrent neural network and artificial neural network: a case study with comparisons
10.1016/j.renene.2020.04.042 · 2020 · External reference
A hybrid deep learning-based neural network for 24-h ahead wind power forecasting
10.1016/j.apenergy.2019.05.044 · 2019 · External reference
Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network
10.1016/j.apenergy.2019.113596 · 2019 · External reference
Generative adversarial networks and convolutional neural networks based weather classification model for day ahead short-term photovoltaic power forecasting
10.1016/j.enconman.2018.11.074 · 2019 · External reference
A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network
10.1016/j.apenergy.2019.113315 · 2019 · External reference
A cascaded deep learning wind power prediction approach based on a two-layer of mode decomposition
10.1016/j.energy.2019.116316 · 2019 · External reference
Forecasting of PV plant output using hybrid wavelet-based LSTM-DNN structure model
10.1049/iet-rpg.2018.5779 · 2019 · External reference
Learning heterogeneous features jointly: a deep end-to-end framework for multi-step short-term wind power prediction
10.1109/tste.2019.2940590 · 2020 · External reference
Wind power prediction using deep neural network based meta regression and transfer learning
10.1016/j.asoc.2017.05.031 · 2017 · External reference
A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets
10.1016/j.enconman.2020.112824 · 2020 · External reference
Interval deep generative neural network for wind speed forecasting
10.1109/tsg.2018.2847223 · 2018 · External reference
Multifactor spatio-temporal correlation model based on a combination of convolutional neural network and long short-term memory neural network for wind speed forecasting
10.1016/j.enconman.2019.02.018 · 2019 · External reference
Short-term prediction of wind power based on deep long short-term memory
2016 · External reference
A multi-variable stacked long-short term memory network for wind speed forecasting
2018 · External reference
Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast
10.1109/access.2020.3024901 · 2020 · External reference
A day-ahead PV power forecasting method based on LSTM-RNN model and time correlation modification under partial daily pattern prediction framework
10.1016/j.enconman.2020.112766 · 2020 · External reference
Hybrid forecasting model based on long short term memory network and deep learning neural network for wind signal
10.1016/j.apenergy.2018.11.063 · 2019 · External reference
LSTM-EFG for wind power forecasting based on sequential correlation features
10.1016/j.future.2018.09.054 · 2019 · External reference
A robust spatiotemporal forecasting framework for photovoltaic generation
10.1109/tsg.2020.3006085 · 2020 · External reference
Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations
10.1016/j.renene.2020.05.150 · 2020 · External reference
Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting
10.1109/access.2019.2921238 · 2019 · External reference
A novel framework for wind speed prediction based on recurrent neural networks and support vector machine
10.1016/j.enconman.2018.10.008 · 2018 · External reference
Wind power forecasting using attention-based gated recurrent unit network
10.1016/j.energy.2020.117081 · 2020 · External reference
Short-term photovoltaic power forecasting based on long short term memory neural network and attention mechanism
10.1109/access.2019.2923006 · 2019 · External reference
Time series forecasting on solar irradiation using deep learning
2017 · External reference
Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting
10.1016/j.energy.2019.01.075 · 2019 · External reference
Accurate photovoltaic power forecasting models using deep LSTM-RNN
10.1007/s00521-017-3225-z · 2019 · External reference
A novel wavenets long short term memory paradigm for wind power prediction
10.1016/j.apenergy.2020.115098 · 2020 · External reference
The short-term interval prediction of wind power using the deep learning model with gradient descend optimization
10.1016/j.renene.2020.03.098 · 2020 · External reference
Distribution-free probability density forecast through deep neural networks
10.1109/tnnls.2019.2907305 · 2019 · External reference
Microgrid energy management system with embedded deep learning forecaster and combined optimizer
10.1109/access.2020.3036131 · 2020 · External reference
Transfer learning for short-term wind speed prediction with deep neural networks
10.1016/j.renene.2015.06.034 · 2016 · External reference
Rough deep neural architecture for short-term wind speed forecasting
10.1109/tii.2017.2730846 · 2017 · External reference
A model combining stacked auto encoder and back propagation algorithm for short-term wind power forecasting
10.1109/access.2018.2818108 · 2018 · External reference
Forecasting of photovoltaic power using deep belief network
2017 · External reference
Deep belief networks with genetic algorithms in forecasting wind speed
10.1109/access.2019.2929542 · 2019 · External reference
Short-term wind speed forecasting via stacked extreme learning machine with generalized correntropy
10.1109/tii.2018.2854549 · 2018 · External reference
Forecasting energy consumption and wind power generation using deep echo state network
10.1016/j.renene.2020.03.042 · 2020 · External reference
Multi-reservoir echo state computing for solar irradiance prediction: a fast yet efficient deep learning approach
10.1016/j.asoc.2020.106481 · 2020 · External reference
A distributed memory RBF-based model for variable generation forecasting
10.1016/j.ijepes.2020.106041 · 2020 · External reference
Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization
10.1016/j.enconman.2018.03.098 · 2018 · External reference
A nonlinear hybrid wind speed forecasting model using LSTM network, hysteretic ELM and Differential Evolution algorithm
10.1016/j.enconman.2018.07.070 · 2018 · External reference
Deep belief network based deterministic and probabilistic wind speed forecasting approach
10.1016/j.apenergy.2016.08.108 · 2016 · External reference
Deep learning based ensemble approach for probabilistic wind power forecasting
10.1016/j.apenergy.2016.11.111 · 2017 · External reference
Deterministic and probabilistic forecasting of photovoltaic power based on deep convolutional neural network
10.1016/j.enconman.2017.10.008 · 2017 · External reference
Deep learning and wavelet transform integrated approach for short-term solar PV power prediction
10.1016/j.measurement.2020.108250 · 2020 · External reference
Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning
10.1016/j.enconman.2019.112418 · 2020 · External reference
Multi-step wind speed prediction based on turbulence intensity and hybrid deep neural networks
10.1016/j.enconman.2019.02.045 · 2019 · External reference
A novel two-stage deep learning wind speed forecasting method with adaptive multiple error corrections and bivariate Dirichlet process mixture model
10.1016/j.enconman.2019.111975 · 2019 · External reference
A hybrid deep learning model for short-term PV power forecasting
10.1016/j.apenergy.2019.114216 · 2020 · External reference
Hybrid deep learning-based model for wind speed forecasting based on DWPT and Bidirectional LSTM network
10.1109/access.2020.3047077 · 2020 · External reference
Hydrogen-based self-sustaining integrated renewable electricity network (HySIREN) using a supply-demand forecasting model and deep-learning algorithms
10.1016/j.enconman.2019.02.017 · 2019 · External reference
A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: a case study of Korea
10.1016/j.rser.2020.109725 · 2020 · External reference
Representation learning with extreme learning machines and empirical mode decomposition for wind speed forecasting methods
10.1016/j.artint.2019.103176 · 2019 · External reference
Research on a novel combination system on the basis of deep learning and swarm intelligence optimization algorithm for wind speed forecasting
10.1109/access.2020.2980562 · 2020 · External reference
Multi-step ahead wind speed prediction based on optimal feature extraction, long short term memory neural network and error correction strategy
10.1016/j.apenergy.2018.08.114 · 2018 · External reference
Effective wind power prediction using novel deep learning network: stacked independently recurrent autoencoder
10.1016/j.renene.2020.09.108 · 2021 · External reference
An improved residual-based convolutional neural network for very short-term wind power forecasting
10.1016/j.enconman.2020.113731 · 2021 · External reference
A hybrid short-term wind power prediction model combining data processing, multiple parameters optimization and multi-intelligent models apportion strategy
10.1109/access.2020.3046001 · 2020 · External reference
Multi-step wind speed forecasting using EWT decomposition, LSTM principal computing, RELM subordinate computing and IEWT reconstruction
10.1016/j.enconman.2018.04.082 · 2018 · External reference
Wind speed prediction method based on empirical wavelet transform and new cell update long short-term memory network
10.1016/j.enconman.2019.06.041 · 2019 · External reference
Wind speed forecasting method based on deep learning strategy using empirical wavelet transform, long short term memory neural network and Elman neural network
10.1016/j.enconman.2017.11.053 · 2018 · External reference
Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks
10.1016/j.renene.2020.09.141 · 2020 · External reference
A combined model based on CEEMDAN, permutation entropy, gated recurrent unit network, and an improved bat algorithm for wind speed forecasting
10.1109/access.2020.3022872 · 2020 · External reference
Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional Gated Recurrent Unit network and Support Vector Regression
10.1016/j.renene.2019.05.039 · 2019 · External reference
A novel combination forecasting model for wind power integrating least square support vector machine, deep belief network, singular spectrum analysis and locality-sensitive hashing
10.1016/j.energy.2018.11.128 · 2019 · External reference
Wind speed prediction based on singular spectrum analysis and neural network structural learning
10.1016/j.enconman.2020.112956 · 2020 · External reference
Multistep forecasting for diurnal wind speed based on hybrid deep learning model with improved singular spectrum decomposition
10.1016/j.enconman.2020.113456 · 2020 · External reference
Day-ahead photovoltaic power forecasting approach based on deep convolutional neural networks and meta learning
10.1016/j.ijepes.2019.105790 · 2020 · External reference
A composite framework coupling multiple feature selection, compound prediction models and novel hybrid swarm optimizer-based synchronization optimization strategy for multi-step ahead short-term wind speed forecasting
10.1016/j.enconman.2019.112461 · 2020 · External reference
Wind speed prediction model using singular spectrum analysis, empirical mode decomposition and convolutional support vector machine
10.1016/j.enconman.2018.11.006 · 2019 · External reference
Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM
10.1016/j.enconman.2018.01.010 · 2018 · External reference
Deterministic and probabilistic multi-step forecasting for short-term wind speed based on secondary decomposition and a deep learning method
10.1016/j.enconman.2020.113098 · 2020 · External reference
Application of hybrid model based on double decomposition, error correction and deep learning in short-term wind speed prediction
10.1016/j.enconman.2019.112345 · 2020 · External reference
Short-term wind speed forecasting with principle-subordinate predictor based on Conv-LSTM and improved BPNN
10.1109/access.2020.2982839 · 2020 · External reference
Wind field-based short-term turbine response forecasting by stacked dilated convolutional LSTMs
10.1109/tste.2019.2954107 · 2020 · External reference
Deep belief network based k-means cluster approach for short-term wind power forecasting
10.1016/j.energy.2018.09.118 · 2018 · External reference
An evolution-dependent multi-objective ensemble model of vanishing moment with adversarial auto-encoder for short-term wind speed forecasting in Xinjiang wind farm, China
10.1016/j.enconman.2019.111914 · 2019 · External reference
A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting
10.1016/j.energy.2020.118773 · 2020 · External reference
Forecasting the high penetration of wind power on multiple scales using multi-to-multi mapping
10.1109/tpwrs.2017.2787667 · 2018 · External reference
Photovoltaic power forecasting with a hybrid deep learning approach
10.1109/access.2020.3025860 · 2020 · External reference
Thermal power forecasting of solar power tower system by combining mechanism modeling and deep learning method
10.1016/j.energy.2020.118403 · 2020 · External reference
Multi-agent microgrid energy management based on deep learning forecaster
10.1016/j.energy.2019.115873 · 2019 · External reference
Photovoltaic power forecasting based LSTM-convolutional network
10.1016/j.energy.2019.116225 · 2019 · External reference
Integrating gray data preprocessor and deep belief network for day-ahead PV power output forecast
10.1109/tste.2018.2888548 · 2018 · External reference
Day-ahead power forecasting in a large-scale photovoltaic plant based on weather classification using LSTM
10.1016/j.energy.2019.07.168 · 2019 · External reference
Improved deep mixture density network for regional wind power probabilistic forecasting
10.1109/tpwrs.2020.2971607 · 2020 · External reference
‘Dust in the wind...’, deep learning application to wind energy time series forecasting
10.3390/en12122385 · 2019 · External reference