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Ling-Ling Li et al.
JOURNAL OF CLEANER PRODUCTION (2020)
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Shahram Hanifi et al.
ENERGIES (2020)
Probabilistic Prediction of Regional Wind Power Based on Spatiotemporal Quantile Regression
Yixiao Yu et al.
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Research on a Novel Combination System on the Basis of Deep Learning and Swarm Intelligence Optimization Algorithm for Wind Speed Forecasting
Xiaohui He et al.
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Markus Reichstein et al.
NATURE (2019)
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Yu Cheng et al.
APPLIED ENERGY (2019)
A novel non-linear combination system for short-term wind speed forecast
Jianzhou Wang et al.
RENEWABLE ENERGY (2019)
The importance of atmospheric turbulence and stability in machine-learning models of wind farm power production
Mike Optis et al.
RENEWABLE & SUSTAINABLE ENERGY REVIEWS (2019)
A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting
Jingjing Song et al.
APPLIED ENERGY (2018)
Smart multi-step deep learning model for wind speed forecasting based on variational mode decomposition, singular spectrum analysis, LSTM network and ELM
Hui Liu et al.
ENERGY CONVERSION AND MANAGEMENT (2018)
Analysis of control-oriented wake modeling tools using lidar field results
Jennifer Annoni et al.
WIND ENERGY SCIENCE (2018)
A combined multivariate model for wind power prediction
Tinghui Ouyang et al.
ENERGY CONVERSION AND MANAGEMENT (2017)
Non-parametric hybrid models for wind speed forecasting
Qinkai Han et al.
ENERGY CONVERSION AND MANAGEMENT (2017)
A novel implementation of kNN classifier based on multi-tupled meteorological input data for wind power prediction
Mehmet Yesilbudak et al.
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Aurelie Fischer et al.
WIND ENERGY (2017)
An improved neural network-based approach for short-term wind speed and power forecast
G. W. Chang et al.
RENEWABLE ENERGY (2017)
Using data-driven approach for wind power prediction: A comparative study
Ehsan Taslimi Renani et al.
ENERGY CONVERSION AND MANAGEMENT (2016)
ARIMA-Based Frequency-Decomposed Modeling of Wind Speed Time Series
Kalid Yunus et al.
IEEE TRANSACTIONS ON POWER SYSTEMS (2016)
Review of Evaluation Criteria and Main Methods of Wind Power Forecasting
Xin Zhao et al.
PROCEEDINGS OF INTERNATIONAL CONFERENCE ON SMART GRID AND CLEAN ENERGY TECHNOLOGIES (ICSGCE 2011) (2011)
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