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Wind power forecasting plays a critical role in modern energy systems, facilitating the integration of renewable energy sources into the power grid.
Overview of the coupled model intercomparison project phase 6 (cmip6) experimental design and organization,
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Time series models to simulate and forecast wind speed and wind power,
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A note on piecewise linear and multilinear table interpolation in many dimensions,
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Using quantile regression to extend an existing wind power forecasting system with probabilistic forecasts,
H. A. Nielsen, H. Madsen, T. S. Nielsen, · 2006
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Analysis of wind power generation and prediction using ANN: A case study,
M. C. Mabel, E. Fernandez, · 2008
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A review on the young history of the wind power short-term prediction,
A. Costa, A. Crespo, J. Navarro, G. Lizcano, H. Madsen, E. Feitosa, · 2008
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Skill forecasting from ensemble predictions of wind power,
P. Pinson, H. A. Nielsen, H. Madsen, G. Kariniotakis, · 2009
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A review on the forecasting of wind speed and generated power,
M. Lei, L. Shiyan, J. Chuanwen, L. Hongling, Z. Yan, · 2009
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From probabilistic forecasts to statistical scenarios of short-term wind power production,
P. Pinson, H. Madsen, H. A. Nielsen, G. Papaefthymiou, B. Klöckl, · 2009
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Accurate short-term wind speed prediction by exploiting diversity in input data using banks of artificial neural networks,
S. Salcedo-Sanz, A. M. Perez-Bellido, E. G. Ortiz-García, A. Portilla-Figueras, L. Prieto, F. Correoso, · 2009
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Climate change impacts on wind energy: A review,
S. C. Pryor, R. J. Barthelmie, · 2010
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Wind energy prediction using a two-hidden layer neural network,
G. Grassi, P. Vecchio, · 2010
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Error analysis of short term wind power prediction models,
M. G. De Giorgi, A. Ficarella, M. Tarantino, · 2011
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Short-term wind speed prediction in wind farms based on banks of support vector machines,
E. G. Ortiz-García, S. Salcedo-Sanz, Á. M. Pérez-Bellido, J. Gascón-Moreno, J. A. Portilla-Figueras, L. Prieto, · 2011
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Fault detection of gas unit of gilan combined cycle power plant using neural network,
A. Forootani, A. Yazdizadeh, A. Aliabadi, · 2011
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Ocean surface wind simulation forced by different reanalyses: Comparison with observed data along the iberian peninsula coast,
D. Carvalho, A. Rocha, M. Gómez-Gesteira, · 2012
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Short-term wind-power prediction based on wavelet transform–support vector machine and statistic-characteristics analysis,
Y. Liu, J. Shi, Y. Yang, W.-J. Lee, · 2012
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AWNN-assisted wind power forecasting using feed-forward neural network,
K. Bhaskar, S. N. Singh, · 2012
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Current methods and advances in forecasting of wind power generation,
A. M. Foley, P. G. Leahy, A. Marvuglia, E. J. McKeogh, · 2012
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Short-term wind power ensemble prediction based on gaussian processes and neural networks,
D. Lee, R. Baldick, · 2013
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A hybrid strategy of short term wind power prediction,
H. Peng, F. Liu, X. Yang, · 2013
Cited alongside, same era.
Short-term wind speed forecasting based on a hybrid model,
W. Zhang, J. Wang, J. Wang, Z. Zhao, M. Tian, · 2013
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T. Hong, P. Pinson, S. Fan, Global energy forecasting competition 2012, 2014
2014
Cited alongside, same era.
Short-term wind power prediction using differential emd and relevance vector machine,
Y. Bao, H. Wang, B. Wang, · 2014
Cited alongside, same era.
A review of combined approaches for prediction of short-term wind speed and power,
A. Tascikaraoglu, M. Uzunoglu, · 2014
Cited alongside, same era.
Current status and future advances for wind speed and power forecasting,
J. Jung, R. P. Broadwater, · 2014
Cited alongside, same era.
Impacts of climate change on wind resources over north america based on na-cordex,
L. Chen, · 2020
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On the suitability of offshore wind energy resource in the united states of america for the 21st century,
X. Costoya, M. DeCastro, D. Carvalho, M. Gómez-Gesteira, · 2020
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Scipy 1.0: fundamental algorithms for scientific computing in python,
P. Virtanen, R. Gommers, T. E. Oliphant, M. Haberland, T. Reddy, D. Cournapeau, E. Burovski, P. Peterson, W. Weckesser, J. Bright, et al., · 2020
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Implicit neural representations with periodic activation functions,
V. Sitzmann, J. Martel, A. Bergman, D. Lindell, G. Wetzstein, · 2020
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Wind and solar intermittency and the associated integration challenges: A comprehensive review including the status in the belgian power system,
S. Asiaban, N. Kayedpour, A. E. Samani, D. Bozalakov, J. D. De Kooning, G. Crevecoeur, L. Vandevelde, · 2021
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Short-term wind power prediction based on lssvm–gsa model,
X. Yuan, C. Chen, Y. Yuan, Y. Huang, Q. Tan, · 2015
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Short-term wind power prediction based on hybrid neural network and chaotic shark smell optimization,
O. Abedinia, N. Amjady, · 2015
Cited alongside, same era.
Wind power forecast using wavelet neural network trained by improved clonal selection algorithm,
H. Chitsaz, N. Amjady, H. Zareipour, · 2015
Cited alongside, same era.
Local models-based regression trees for very short-term wind speed prediction,
A. Troncoso, S. Salcedo-Sanz, C. Casanova-Mateo, J. Riquelme, L. Prieto, · 2015
Cited alongside, same era.
Future changes of wind energy potentials over europe in a large cmip5 multi-model ensemble.,
M. Reyers, J. Moemken, J. G. Pinto, · 2016
Cited alongside, same era.
Potential impacts of climate change on european wind energy resource under the cmip5 future climate projections,
D. Carvalho, A. Rocha, M. Gómez-Gesteira, C. S. Santos, · 2017
Cited alongside, same era.
Climate change impacts on the future offshore wind energy resource in china,
X. Costoya, M. DeCastro, D. Carvalho, Z. Feng, M. Gómez-Gesteira, · 2021
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Projected changes in wind speed and wind energy potential over west africa in cmip6 models,
A. A. Akinsanola, K. O. Ogunjobi, A. T. Abolude, S. Salack, · 2021
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Accelerating deployment of offshore wind energy alter wind climate and reduce future power generation potentials,
N. Akhtar, B. Geyer, B. Rockel, P. S. Sommer, C. Schrum, · 2021
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Wind energy resource over europe under CMIP6 future climate projections: What changes from CMIP5 to CMIP6,
D. Carvalho, A. Rocha, X. Costoya, M. DeCastro, M. Gómez-Gesteira, · 2021
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Cmip6 simulations with the cmcc earth system model (cmcc-esm2),
T. Lovato, D. Peano, M. Butenschön, S. Materia, D. Iovino, E. Scoccimarro, P. Fogli, A. Cherchi, A. Bellucci, S. Gualdi, et al., · 2022
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Power generation from variable renewable energies (VRE),
R. Lehneis, D. Manske, B. Schinkel, D. Thrän, · 2022
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Well performance prediction based on long short-term memory (lstm) neural network,
R. Huang, C. Wei, B. Wang, J. Yang, X. Xu, S. Wu, S. Huang, · 2022
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A cmip6-based multi-model downscaling ensemble to underpin climate change services in australia,
M. R. Grose, S. Narsey, R. Trancoso, C. Mackallah, F. Delage, A. Dowdy, G. Di Virgilio, I. Watterson, P. Dobrohotoff, H. A. Rashid, et al., · 2023
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A robust sindy approach by combining neural networks and an integral form,
A. Forootani, P. Goyal, P. Benner, · 2023
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Temporally and spatially resolved simulation of the wind power generation in germany,
R. Lehneis, D. Thrän, · 2023
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A high-altitude wind resource assessment method for decentralized wind power based on improved linear regression,
L. Zhang, W. Song, E. Sun, Q. Zhang, D. Wu, F. Chen, Y. Liu, · 2024
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Bio-eng-lmm ai assist chatbot: A comprehensive tool for research and education,
A. Forootani, D. E. Aliabadi, D. Thraen, · 2024
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A. Forootani, P. Benner, · 2024
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Gs-pinn: Greedy sampling for parameter estimation in partial differential equations,
A. Forootani, H. Kapadia, S. Chellappa, P. Goyal, P. Benner, · 2024
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