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Neural networks-based learning of the distribution of non-dispatchable renewable electricity generation from sources such as photovoltaics (PV) and wind as well as load demands has recently gained attention.
On lines and planes of closest fit to systems of points in space
Pearson, K. (1901) · 1901
Earlier work this paper cites.
Forecasting spatio-temporal renewable scenarios: A deep generative approach
Jiang, C., Chen, Y., Mao, Y., Chai, Y., and Yu, M. (2019) · 1903
Earlier work this paper cites.
Normalizing flows: An introduction and review of current methods
Kobyzev, I., Prince, S. J. D., and Brubaker, M. A. (2020) · 1908
Earlier work this paper cites.
Normalizing flows for probabilistic modeling and inference
Papamakarios, G., Nalisnick, E., Rezende, D. J., Mohamed, S., and Lakshminarayanan, B. (2019) · 1912
Earlier work this paper cites.
The significance probability of the Smirnov two-sample test
Hodges, J. L. (1958) · 1958
Earlier work this paper cites.
On estimation of a probability density function and mode
Parzen, E. (1962) · 1962
Earlier work this paper cites.
Models for forecasting seasonal and non-seasonal time series
Box, G. E., Jenkins, G. M., and Bacon, D. W. (1967) · 1967
Earlier work this paper cites.
The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms
Welch, P. (1967) · 1967
Earlier work this paper cites.
Nonlinear independent component analysis: Existence and uniqueness results
Hyvärinen, A. and Pajunen, P. (1999) · 1999
Earlier work this paper cites.
Probabilistic principal component analysis
Tipping, M. E. and Bishop, C. M. (1999) · 1999
Earlier work this paper cites.
Nonlinear dimensionality reduction by locally linear embedding
Roweis, S. T. and Saul, L. K. (2000) · 2000
Earlier work this paper cites.
Flows for simultaneous manifold learning and density estimation
Brehmer, J. and Cranmer, K. (2020) · 2003
Earlier work this paper cites.
Evaluation of scenario-generation methods for stochastic programming
Kaut, M. and Wallace, S. W. (2003) · 2003
Earlier work this paper cites.
Diffusion maps
Coifman, R. R. and Lafon, S. (2006) · 2006
Earlier work this paper cites.
From probabilistic forecasts to statistical scenarios of short-term wind power production
Pinson, P., Madsen, H., Nielsen, H. A., Papaefthymiou, G., and Klöckl, B. (2009) · 2009
Earlier work this paper cites.
Introduction to stochastic programming
Birge, J. R. and Louveaux, F. (2011) · 2011
Earlier work this paper cites.
Shape-based scenario generation using copulas
Kaut, M. and Wallace, S. W. (2011) · 2011
Earlier work this paper cites.
Scikit-learn: Machine learning in Python
Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., Blondel, M., Prettenhofer, P., Weiss, R., Dubourg, V., Vanderplas, J., Passos, A., Cournapeau, D., Brucher, M., Perrot, M., and Duchesnay, E. (2011) · 2011
Earlier work this paper cites.
Consistency of the kernel density estimator: a survey
Wied, D. and Weißbach, R. (2012) · 2012
Cited alongside, same era.
Integrating Renewables in Electricity Markets: Operational Problems
Morales, J. M., Conejo, A. J., Madsen, H., Pinson, P., and Zugno, M. (2013) · 2013
Cited alongside, same era.
Wind power scenario generation and reduction in stochastic programming framework
Sharma, K. C., Jain, P., and Bhakar, R. (2013) · 2013
Cited alongside, same era.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014) · 2014
Cited alongside, same era.
Auto-encoding variational bayes
Kingma, D. P. and Welling, M. (2014) · 2014
Cited alongside, same era.
A review of combined approaches for prediction of short-term wind speed and power
Challenges in process optimization for new feedstocks and energy sources
Mitsos, A., Asprion, N., Floudas, C. A., Bortz, M., Baldea, M., Bonvin, D., Caspari, A., and Schäfer, P. (2018) · 2018
Later among the works it cites.
A conditional model of wind power forecast errors and its application in scenario generation
Wang, Z., Shen, C., and Liu, F. (2018) · 2018
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Optimized operation of cascade reservoirs considering complementary characteristics between wind and photovoltaic based on variational auto-encoder
Zhanga, H., Hua, W., Yub, R., Tangb, M., and Dingc, L. (2018) · 2018
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Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks
Ziel, F. and Weron, R. (2018) · 2018
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The European power sector in 2019: Up-to-date analysis on the electricity transition
Agora Energiewende and Sandbag (2020) · 2019
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Tascikaraoglu, A. and Uzunoglu, M. (2014) · 2014
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TensorFlow: Large-scale machine learning on heterogeneous systems
Abadi, M. and Agarwal, A. (2015) · 2015
Cited alongside, same era.
NICE: Non-linear independent components estimation
Dinh, L., Krueger, D., and Bengio, Y. (2015) · 2015
Cited alongside, same era.
Testing the manifold hypothesis
Fefferman, C., Mitter, S., and Narayanan, H. (2016) · 2016
Cited alongside, same era.
Normalizing flows on Riemannian manifolds
Gemici, M. C., Rezende, D., and Mohamed, S. (2016) · 2016
Cited alongside, same era.
Sampling via measure transport: An introduction
Marzouk, Y., Moselhy, T., Parno, M., and Spantini, A. (2016) · 2016
Cited alongside, same era.
ANN-based scenario generation methodology for stochastic variables of electric power systems
Vagropoulos, S. I., Kardakos, E. G., Simoglou, C. K., Bakirtzis, A. G., and Catalao, J. P. (2016) · 2016
Cited alongside, same era.
GAN-based model for residential load generation considering typical consumption patterns
Gu, Y., Chen, Q., Liu, K., Xie, L., and Kang, C. (2019) · 2019
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Data-driven EV load profiles generation using a variational auto-encoder
Pan, Z., Wang, J., Liao, W., Chen, H., Yuan, D., Zhu, W., Fang, X., and Zhu, Z. (2019) · 2019
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Generative adversarial networks for operational scenario planning of renewable energy farms: A study on wind and photovoltaic
Schreiber, J., Jessulat, M., and Sick, B. (2019) · 2019
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Short-term optimal operation of hydro-wind-solar hybrid system with improved generative adversarial networks
Wei, H., Hongxuan, Z., Yu, D., Yiting, W., Ling, D., and Ming, X. (2019) · 2019
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Scenario generation in stochastic programming using principal component analysis based on moment-matching approach
Chopra, I. and Selvamuthu, D. (2020) · 2020
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Modeling daily load profiles of distribution network for scenario generation using flow-based generative network
Ge, L., Liao, W., Wang, S., Bak-Jensen, B., and Pillai, J. R. (2020) · 2020
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Time series
Open power systems data (2019) · 2020
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Optimal configuration of concentrating solar power in multienergy power systems with an improved variational autoencoder
Qi, Y., Hu, W., Dong, Y., Fan, Y., Dong, L., and Xiao, M. (2020) · 2020
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Scenario forecasting of residential load profiles
Zhang, L. and Zhang, B. (2020) · 2020
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Typical wind power scenario generation for multiple wind farms using conditional improved Wasserstein generative adversarial network
Zhang, Y., Ai, Q., Xiao, F., Hao, R., and Lu, T. (2020) · 2020
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Understanding and mitigating exploding inverses in invertible neural networks
Behrmann, J., Vicol, P., Wang, K.-C., Grosse, R., and Jacobsen, J.-H. (2021) · 2021
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A deep generative model for probabilistic energy forecasting in power systems: Normalizing flows
Dumas, J., Lanaspeze, A. W. D., Cornélusse, B., and Sutera, A. (2021) · 2021
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