|
[1]
|
Relative evaluation of probabilistic methods for spatio-temporal wind forecasting
Journal of Cleaner Production,
2024
DOI:10.1016/j.jclepro.2023.139944
|
|
|
|
|
[2]
|
Prediction system for distribution wind speed using deep learning algorithm
INTERNATIONAL CONFERENCE ON SCIENTIFIC RESEARCH & INNOVATION (ICSRI 2022),
2023
DOI:10.1063/5.0151097
|
|
|
|
|
[3]
|
kNN Classifier Applications in Wind and Solar Energy Systems
2022 11th International Conference on Renewable Energy Research and Application (ICRERA),
2022
DOI:10.1109/ICRERA55966.2022.9922701
|
|
|
|
|
[4]
|
Improved extreme learning machine with AutoEncoder and particle swarm optimization for short-term wind power prediction
Neural Computing and Applications,
2022
DOI:10.1007/s00521-021-06619-x
|
|
|
|
|
[5]
|
A review of ultra-short-term forecasting of wind power based on data decomposition-forecasting technology combination model
Energy Reports,
2022
DOI:10.1016/j.egyr.2022.10.342
|
|
|
|
|
[6]
|
A review of ultra-short-term forecasting of wind power based on data decomposition-forecasting technology combination model
Energy Reports,
2022
DOI:10.1016/j.egyr.2022.10.342
|
|
|
|
|
[7]
|
A review of ultra-short-term forecasting of wind power based on data decomposition-forecasting technology combination model
Energy Reports,
2022
DOI:10.1016/j.egyr.2022.10.342
|
|
|
|
|
[8]
|
Wind Power Forecasting Model Based on Extreme Learning Machine and Time series
2021 Fifth International Conference On Intelligent Computing in Data Sciences (ICDS),
2021
DOI:10.1109/ICDS53782.2021.9626737
|
|
|
|
|
[9]
|
Wind power prediction using a three stage genetic ensemble and auxiliary predictor
Applied Soft Computing,
2020
DOI:10.1016/j.asoc.2020.106151
|
|
|
|
|
[10]
|
On-farm welfare monitoring system for goats based on Internet of Things and machine learning
International Journal of Distributed Sensor Networks,
2020
DOI:10.1177/1550147720944030
|
|
|
|
|
[11]
|
A review and discussion of decomposition-based hybrid models for wind energy forecasting applications
Applied Energy,
2019
DOI:10.1016/j.apenergy.2018.10.080
|
|
|
|
|
[12]
|
Analysis of high-dimensional biomedical data using an evolutionary multi-objective emperor penguin optimizer
Swarm and Evolutionary Computation,
2019
DOI:10.1016/j.swevo.2019.04.010
|
|
|
|
|
[13]
|
A Hybrid Model Based on a Two-Layer Decomposition Approach and an Optimized Neural Network for Chaotic Time Series Prediction
Symmetry,
2019
DOI:10.3390/sym11050610
|
|
|
|
|
[14]
|
Dealing with seasonality by narrowing the training set in time series forecasting with k NN
Expert Systems with Applications,
2018
DOI:10.1016/j.eswa.2018.03.005
|
|
|
|
|
[15]
|
A novel framework for wind speed prediction based on recurrent neural networks and support vector machine
Energy Conversion and Management,
2018
DOI:10.1016/j.enconman.2018.10.008
|
|
|
|
|
[16]
|
Multidimensionalk-nearest neighbor model based on EEMD for financial time series forecasting
Physica A: Statistical Mechanics and its Applications,
2017
DOI:10.1016/j.physa.2017.02.072
|
|
|
|
|
[17]
|
Comparative study on three new hybrid models using Elman Neural Network and Empirical Mode Decomposition based technologies improved by Singular Spectrum Analysis for hour-ahead wind speed forecasting
Energy Conversion and Management,
2017
DOI:10.1016/j.enconman.2017.05.008
|
|
|
|
|
[18]
|
Hybrid multiscale wind speed forecasting based on variational mode decomposition
International Transactions on Electrical Energy Systems,
2017
DOI:10.1002/etep.2466
|
|
|
|
|
[19]
|
A methodology for applying k-nearest neighbor to time series forecasting
Artificial Intelligence Review,
2017
DOI:10.1007/s10462-017-9593-z
|
|
|
|
|
[20]
|
Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
IEEE Computational Intelligence Magazine,
2016
DOI:10.1109/MCI.2015.2471235
|
|
|
|
|
[21]
|
Ultra-short-term wind speed prediction based on multi-scale predictability analysis
Cluster Computing,
2016
DOI:10.1007/s10586-016-0554-0
|
|
|
|
|
[22]
|
A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods
IEEE Transactions on Sustainable Energy,
2015
DOI:10.1109/TSTE.2014.2365580
|
|
|
|
|
[23]
|
Ensemble methods for wind and solar power forecasting—A state-of-the-art review
Renewable and Sustainable Energy Reviews,
2015
DOI:10.1016/j.rser.2015.04.081
|
|
|
|
|
[24]
|
A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods
IEEE Transactions on Sustainable Energy,
2015
DOI:10.1109/TSTE.2014.2365580
|
|
|
|
|
[25]
|
Empirical mode decomposition based adaboost-backpropagation neural network method for wind speed forecasting
2014 IEEE Symposium on Computational Intelligence in Ensemble Learning (CIEL),
2014
DOI:10.1109/CIEL.2014.7015741
|
|
|
|