- Antipov, G. ; Baccouche, M. ; Dugelay, J.L. Face aging with conditional generative adversarial networks[C]. 2017 IEEE international conference on image processing (ICIP). 2017 IEEE:
Paper not yet in RePEc: Add citation now
- Arjovsky, M. ; Chintala, S. ; Bottou, L. Wasserstein generative adversarial networks[C]. International conference on machine learning. 2017 PMLR:
Paper not yet in RePEc: Add citation now
- Becker, R. Generation of time-coupled wind power infeed scenarios using pair-copula construction. 2017 IEEE Trans Sustain Energy. 9 1298-1306
Paper not yet in RePEc: Add citation now
- Chen, Y. ; Wang, Y. ; Kirschen, D. ; Zhang, B. Model-free renewable scenario generation using generative adversarial networks. 2018 IEEE Power Syst. 33 3265-3275
Paper not yet in RePEc: Add citation now
- Cheng, L. Augmented convolutional network for wind power prediction: a new recurrent architecture design with spatial-temporal image inputs. 2021 IEEE Trans Ind Inf. 17 6981-6993
Paper not yet in RePEc: Add citation now
- Cho, Y.H. Wind power scenario generation using graph convolutional generative adversarial network[C]. IEEE power & energy society general meeting (PESGM). 2023 IEEE. 1-5
Paper not yet in RePEc: Add citation now
- Dai, J. ; He, K. ; Li, Y. ; Ren, S. ; Sun, J. Instance-sensitive fully convolutional networks. 2016 Lect Notes Comput Sci. 534-549
Paper not yet in RePEc: Add citation now
Dong, W. ; Chen, X. ; Yang, Q. Data-driven scenario generation of renewable energy production based on controllable generative adversarial networks with interpretability. 2022 Appl Energy. 308 -
- Gan, Z. ; Li, C. ; Zhou, J. ; Tang, G. Temporal convolutional networks interval prediction model for wind speed forecasting. 2021 Elec Power Syst Res. 191 -
Paper not yet in RePEc: Add citation now
- Gao, J. MTGNN: multi-task graph neural network based few-shot learning for disease similarity measurement. 2022 Methods. 198 88-95
Paper not yet in RePEc: Add citation now
- Goodfellow, I. Generative adversarial networks. 2014 Conf Neural Inform Processing Syst. 2672-2680
Paper not yet in RePEc: Add citation now
- He, X. ; Nie, Y. ; Guo, H. ; Wang, J. Research on a novel combination system on the basis of deep learning and swarm intelligence optimization algorithm for wind speed forecasting. 2020 IEEE Access. 8 51482-51499
Paper not yet in RePEc: Add citation now
- Jiang, C. ; Mao, Y. ; Chai, Y. ; Yu, M. ; Tao, S. Scenario generation for wind power using improved generative adversarial networks. 2018 IEEE Access. 6 62193-62203
Paper not yet in RePEc: Add citation now
- Ke, L. An efficient wind speed prediction method based on a deep neural network without future information leakage. 2023 Energy. 267 -
Paper not yet in RePEc: Add citation now
- Kim, T.Y. ; Cho, S.B. Predicting residential energy consumption using CNN-LSTM neural networks. 2019 Energy. 182 72-81
Paper not yet in RePEc: Add citation now
- Kipf, T.N. ; Welling, M. Semi-supervised classification with graph convolutional networks[J] ICLR. 2017 :
Paper not yet in RePEc: Add citation now
Krishna, A.B. ; Abhyankar, A.R. Time-coupled day-ahead wind power scenario generation: a combined regular vine copula and variance reduction method. 2023 Energy. 265-
- Liu, X. ; Yang, L. ; Zhang, Z. Short-term multi-step ahead wind power predictions based on A novel deep convolutional recurrent network method. 2021 IEEE Trans Sustain Energy. 12 1820-1833
Paper not yet in RePEc: Add citation now
- Ma, X. ; Sun, Y. ; Fang, H. Scenario generation of wind power based on statistical uncertainty and variability. 2013 IEEE Trans Sustain Energy. 4 894-904
Paper not yet in RePEc: Add citation now
- Morales, J.M. ; Conejo, A.J. ; Madsen, H. ; Pinson, P. ; Zugno, M. Integrating renewables in electricity markets: operational problems. 2013 Springer Science & Business Media:
Paper not yet in RePEc: Add citation now
- Niu, D. ; Sun, L. ; Yu, M. ; Wang, K. Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model. 2022 Energy. 254 1-12
Paper not yet in RePEc: Add citation now
Santos, V.O. ; Rocha, P.A.C. ; Scott, J. ; Griensven, J.V. ; Gharabaghi, B. Spatiotemporal analysis of bidimensional wind speed forecasting: development and thorough assessment of LSTM and ensemble graph neural networks on the Dutch database. 2023 Energy. 278 -
- Sideratos, G. ; Hatziargyriou, N.D. Probabilistic wind power forecasting using radial basis function neural networks. 2012 IEEE Trans Power Syst. 27 1788-1796
Paper not yet in RePEc: Add citation now
Song, D. ; Li, Z. ; Wang, L. Energy capture efficiency enhancement of wind turbines via stochastic model predictive yaw control based on intelligent scenarios generation. 2022 Appl Energy. 312 -
- Stappers, B. ; Paterakis, N.G. ; Kok, J.K. ; Gibescu, M. A class-driven approach based on long short-term memory networks for electricity price scenario generation and reduction. 2020 IEEE Trans Power Syst. 35 3040-3050
Paper not yet in RePEc: Add citation now
Sun, M. ; Cremer, J. ; Strbac, G. A novel data-driven scenario generation framework for transmission expansion planning with high renewable energy penetration. 2018 Appl Energy. 228 546-555
- Tawn, R. ; Browell, J. ; Dinwoodie, I. Missing data in wind farm time series: properties and effect on forecasts. 2020 Elec Power Syst Res. 189 -
Paper not yet in RePEc: Add citation now
Wang, H. Deep learning based ensemble approach for probabilistic wind power forecasting. 2017 Appl Energy. 188 56-70
- Wang, L. Effective wind power prediction using novel deep learning network: stacked independently recurrent autoencoder. 2021 Renew Energy. 164 642-655
Paper not yet in RePEc: Add citation now
- Wang, Y. ; Wei, S. ; Yang, W. ; Chai, Y. Robust active yaw control for offshore wind farms using stochastic predictive control based on online adaptive scenario generation. 2023 Ocean Eng. 286 -
Paper not yet in RePEc: Add citation now
- Wen, P. ; Zhang, S. ; Xing, Y. ; Huo, L. ; Bohlooli, N. A novel method based on lower–upper bound approximation to predict the wind energy. 2020 J Clean Prod. 259 -
Paper not yet in RePEc: Add citation now
- Zhang, G. ; Li, H.X. ; Gan, M. Design a wind speed prediction model using probabilistic fuzzy system. 2012 IEEE Trans Ind Inf. 8 819-827
Paper not yet in RePEc: Add citation now
- Zhang, Y. ; Ai, Q. ; Xiao, F. ; Hao, R. ; Lu, T. Typical wind power scenario generation for multiple wind farms using conditional improved wasserstein generative adversarial network. 2020 Elec Power Syst Res. 114 -
Paper not yet in RePEc: Add citation now
- Zhang, Z. ; Cui, P. ; Zhu, W. Deep learning on graphs: a survey. 2022 IEEE Trans Knowl Data Eng. 34 249-270
Paper not yet in RePEc: Add citation now
- Zheng, Z. Generative probabilistic wind speed forecasting: a variational recurrent autoencoder based method. 2021 IEEE Trans Power Syst. 37 1386-1398
Paper not yet in RePEc: Add citation now
- Zhou, J. ; Cui, G. ; Hu, S. Graph neural networks: a review of methods and applications. 2020 AI open. 1 57-81
Paper not yet in RePEc: Add citation now
- Zhu, R. ; Liao, W. ; Wang, Y. Short-term prediction for wind power based on temporal convolutional network. 2020 Energy Rep. 424-429
Paper not yet in RePEc: Add citation now