1. Computationally efficient data synthesis for AC-OPF: Integrating Physics-Informed Neural Network solvers and active learning. (2025). Wu, Chenye ; Lu, Chenbei ; Peng, Ruo ; Zhang, Jiahao.
   In: Applied Energy.
   RePEc:eee:appene:v:378:y:2025:i:pa:s030626192402097x.

   Full description at Econpapers || Download paper

2. A framework of data assimilation for wind flow fields by physics-informed neural networks. (2024). Sun, Zhenxu ; Lutz, Thorsten ; Xu, Shengfeng ; Yang, Guowei ; Guo, Dilong.
   In: Applied Energy.
   RePEc:eee:appene:v:371:y:2024:i:c:s0306261924011024.

   Full description at Econpapers || Download paper

1. Acikgoz, H. ; Budak, U. ; Korkmaz, D. ; Yildiz, C. WSFNet: an efficient wind speed forecasting model using channel attention-based densely connected convolutional neural network. 2021 Energy. 233 -

2. Aliakbari, M. ; Mahmoudi, M. ; Vadasz, P. ; Arzani, A. Predicting high-fidelity multiphysics data from low-fidelity fluid flow and transport solvers using physics-informed neural networks. 2022 Int J Heat Fluid Flow. 96 -
   Paper not yet in RePEc: Add citation now
   

3. Antonini, E.G.A. ; Romero, D.A. ; Amon, C.H. Improving CFD wind farm simulations incorporating wind direction uncertainty. 2019 Renew Energy. 133 1011-1023

4. Baldan, M. ; Di Barba, P. ; Lowther, D.A. Physics-informed neural networks for inverse electromagnetic problems. 2023 IEEE Trans Magn. 59 1-5
   Paper not yet in RePEc: Add citation now
   
5. Bartl, J. ; Mühle, F. ; Schottler, J. ; Sætran, L. ; Peink, J. ; Adaramola, M. Wind tunnel experiments on wind turbine wakes in yaw: effects of inflow turbulence and shear. 2018 Wind Energy Sci. 3 329-343
   Paper not yet in RePEc: Add citation now
   
6. Baydin, A.G. ; Pearlmutter, B.A. ; Radul, A.A. ; Siskind, J.M. Automatic differentiation in machine learning: a survey. 2018 J Mach Learn Res. 18 1-43
   Paper not yet in RePEc: Add citation now
   
7. Bengio, Y. ; Simard, P. ; Frasconi, P. Learning long-term dependencies with gradient descent is difficult. 1994 IEEE Trans Neural Netw Learn Syst. 5 157-166
   Paper not yet in RePEc: Add citation now
   
8. Borraccino, A. ; Schlipf, D. ; Haizmann, F. ; Wagner, R. Wind field reconstruction from nacelle-mounted lidar short-range measurements. 2017 Wind Energy Sci. 2 269-283
   Paper not yet in RePEc: Add citation now
   
9. Cai, S. ; Mao, Z. ; Wang, Z. Physics-informed neural networks (PINNs) for fluid mechanics: a review. 2021 Acta Mech Sin. 37 1727-1738
   Paper not yet in RePEc: Add citation now
   
10. Carcangiu, C.E. ; Pujana-Arrese, A. ; Mendizabal, A. ; Pinede, I. ; Landaluze, J. Wind gust detection and load mitigation using artificial neural networks assisted control. 2014 Wind Energy. 17 957-970
    Paper not yet in RePEc: Add citation now
    
11. Cheng, C. ; Zhang, G.T. Deep learning method based on physics informed neural network with resnet block for solving fluid flow problems. 2021 Water. 13 423-
    Paper not yet in RePEc: Add citation now
    
12. Dolatabadi, A. ; Abdeltawab, H. ; Mohamed, Y.A.R.I. Deep spatial-temporal 2-D CNN-BLSTM model for ultrashort-term LiDAR-assisted wind turbine’s power and fatigue load forecasting. 2021 IEEE Trans on Industr Inform. 18 2342-2353
    Paper not yet in RePEc: Add citation now
    

13. Fleming, P.A. ; Gebraad, P.M.O. ; Lee, S. ; van Wingerden, J.W. ; Johnson, K. ; Churchfield, M. Evaluating techniques for redirecting turbine wakes using SOWFA. 2014 Renew Energy. 70 211-218

14. Glorot, X. ; Bengio, Y. Understanding the difficulty of training deep feedforward neural networks. 2010 En : Proceedings of the thirteenth international conference on artificial intelligence and statistics. :
    Paper not yet in RePEc: Add citation now
    
15. Harris, M. ; Hand, M. ; Wright, A. Lidar for turbine control. Report no. NREL/TP-500-39154. 2016 Golden, CO: National Renewable Energy Laboratory
    Paper not yet in RePEc: Add citation now
    
16. Hayou, S. ; Clerico, E. ; He, B. ; Deligiannidis, G. ; Doucet, A. ; Rousseau, J. Stable resnet. 2021 En : International conference on artificial intelligence and statistics. PMLR:
    Paper not yet in RePEc: Add citation now
    
17. He, K. ; Zhang, X. ; Ren, S. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. 2015 En : Proceedings of the IEEE international conference on computer vision. IEEE:
    Paper not yet in RePEc: Add citation now
    
18. He, K. ; Zhang, X. ; Ren, S. ; Sun, J. Deep residual learning for image recognition. 2016 En : Proceedings of the IEEE conference on computer vision and pattern recognition. IEEE:
    Paper not yet in RePEc: Add citation now
    
19. He, K. ; Zhang, X. ; Ren, S. ; Sun, J. Identity mappings in deep residual networks. 2016 En : European conference on computer vision. Springer:
    Paper not yet in RePEc: Add citation now
    

20. He, R. ; Yang, H. ; Sun, S. A machine learning-based fatigue loads and power prediction method for wind turbines under yaw control. 2022 Appl Energy. 326 -

21. Huang, B. ; Wang, J. Applications of physics-informed neural networks in power systems-a review. 2022 IEEE Trans Power Syst. 38 572-588
    Paper not yet in RePEc: Add citation now
    
22. Jagtap, A.D. ; Kawaguchi, K. ; Karniadakis, G.E. Adaptive activation functions accelerate convergence in deep and physics-informed neural networks. 2020 J Comput Phys. 404 -
    Paper not yet in RePEc: Add citation now
    
23. Jin, X. ; Cai, S. ; Li, H. NSFnets (Navier-Stokes flow nets): physics-informed neural networks for the incompressible Navier-stokes equations. 2021 J Comput Phys. 426 -
    Paper not yet in RePEc: Add citation now
    
24. Khan, A. ; Lowther, D.A. Physics informed neural networks for electromagnetic analysis. 2022 IEEE Trans Magn. 58 1-4
    Paper not yet in RePEc: Add citation now
    
25. Li, J. ; Yu, X.B. LiDAR technology for wind energy potential assessment: demonstration and validation at a site around Lake Erie. 2017 Energ Conver Manage. 144 252-261
    Paper not yet in RePEc: Add citation now
    
26. Li, R. ; Zhang, J. ; Zhao, X. Multi-fidelity modeling of wind farm wakes based on a novel super-fidelity network. 2022 Energ Conver Manage. 270 -
    Paper not yet in RePEc: Add citation now
    
27. Liu, Z. A review of progress and applications of pulsed Doppler wind LiDARs. 2019 Remote Sens (Basel). 11 2522-
    Paper not yet in RePEc: Add citation now
    
28. Mahdizadeh, A. ; Schmid, R. ; Oetomo, D. Lidar-assisted exact output regulation for load mitigation in wind turbines. 2020 IEEE Trans Control Syst Technol. 29 1102-1116
    Paper not yet in RePEc: Add citation now
    
29. Mao, Z. ; Jagtap, A.D. ; Karniadakis, G.E. Physics-informed neural networks for high-speed flows. 2020 Comput Methods Appl Mech Eng. 360 -
    Paper not yet in RePEc: Add citation now
    
30. Mehta, D. ; Van Zuijlen, A.H. ; Koren, B. ; Holierhoek, J.G. ; Bijl, H. Large Eddy simulation of wind farm aerodynamics: a review. 2014 J Wind Eng Ind Aerodyn. 133 1-17
    Paper not yet in RePEc: Add citation now
    
31. Mercieca, J. ; Aram, P. ; Jones, B.L. ; Kadirkamanathan, V. A spatiotemporal estimation framework for real-world lidar wind speed measurements. 2019 IEEE Trans Control Syst Technol. 28 1595-1602
    Paper not yet in RePEc: Add citation now
    
32. Mohammadian, M. ; Baker, K. ; Fioretto, F. Gradient-enhanced physics-informed neural networks for power systems operational support. 2023 Electr Pow Syst Res. 223 -
    Paper not yet in RePEc: Add citation now
    
33. Nazari, L.F. ; Camponogara, E. ; Seman, L.O. Physics-informed neural networks for modeling water flows in a river channel. 2022 IEEE Trans Artif Intell. -
    Paper not yet in RePEc: Add citation now
    
34. Raissi, M. ; Perdikaris, P. ; Karniadakis, G.E. Physics-informed neural networks: a deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations. 2019 J Comput Phys. 378 686-707
    Paper not yet in RePEc: Add citation now
    
35. Roy, S.K. ; Manna, S. ; Song, T. ; Bruzzone, L. Attention-based adaptive spectral–spatial kernel ResNet for hyperspectral image classification. 2020 IEEE Trans Geosci Remote Sens. 59 7831-7843
    Paper not yet in RePEc: Add citation now
    
36. Santoni, C. ; Ciri, U. ; Rotea, M. ; Leonardi, S. Development of a high fidelity CFD code for wind farm control. 2015 En : 2015 American control conference. IEEE:
    Paper not yet in RePEc: Add citation now
    
37. Sharma, P.K. ; Warudkar, V. ; Ahmed, S. Application of lidar and measure correlate predict method in offshore wind resource assessments. 2019 J Clean Prod. 215 534-543
    Paper not yet in RePEc: Add citation now
    
38. Son, S. ; Lee, H. ; Jeong, D. A novel physics-informed neural network for modeling electromagnetism of a permanent magnet synchronous motor. 2023 Adv Eng Inform. 57 -
    Paper not yet in RePEc: Add citation now
    
39. Sun, S. ; Liu, S. ; Liu, J. Wind field reconstruction using inverse process with optimal sensor placement. 2018 IEEE Trans Sustain Energy. 10 1290-1299
    Paper not yet in RePEc: Add citation now
    

40. Sun, S. ; Wang, T. ; Yang, H. Condition monitoring of wind turbine blades based on self-supervised health representation learning: a conducive technique to effective and reliable utilization of wind energy. 2022 Appl Energy. 313 -

41. Towers, P. ; Jones, B.L. Real-time wind field reconstruction from LiDAR measurements using dynamic wind model and state estimation. 2016 Wind Energy. 19 133-150
    Paper not yet in RePEc: Add citation now
    
42. Vaswani, A. ; Shazeer, N. ; Parmar, N. ; Uszkoreit, J. ; Jones, L. ; Gomez, A.N. Attention is all you need. 2017 En : Advances in neural information processing systems 30 (NIPS2017). :
    Paper not yet in RePEc: Add citation now
    
43. Wagner, R. ; Courtney, M.S. ; Pedersen, T.F. ; Davoust, S. Uncertainty of power curve measurement with a two-beam nacelle-mounted lidar. 2016 Wind Energy. 19 1269-1287
    Paper not yet in RePEc: Add citation now
    

44. Wang, J. ; Wang, S. ; Zeng, B. A novel ensemble probabilistic forecasting system for uncertainty in wind speed. 2022 Appl Energy. 313 -

45. Wang, J. ; Wang, Y. ; Gao, X. Solution of tidal equations and inversion of bottom friction coefficient based on neural network. 2022 En : Proceedings of OCEANS 2022. IEEE:
    Paper not yet in RePEc: Add citation now
    
46. Wang, N. ; Johnson, K.E. ; Wright, A.D. Comparison of strategies for enhancing energy capture and reducing loads using LIDAR and feedforward control. 2013 IEEE Trans Control Syst Technol. 21 1129-1142
    Paper not yet in RePEc: Add citation now
    
47. Wang, S. ; Teng, Y. ; Perdikaris, P. Understanding and mitigating gradient flow pathologies in physics-informed neural networks. 2021 SIAM J Sci Comput. 43 A3055-A3081
    Paper not yet in RePEc: Add citation now
    
48. Wang, Y. ; Yuan, Z. ; Xie, C. ; Wang, J. Temporally sparse data assimilation for the small-scale reconstruction of turbulence. 2022 Phys Fluids. 34 -
    Paper not yet in RePEc: Add citation now
    
49. Yan, B.W. ; Li, Q.S. Coupled on-site measurement/CFD based approach for high-resolution wind resource assessment over complex terrains. 2016 Energ Conver Manage. 117 351-366
    Paper not yet in RePEc: Add citation now
    
50. Yang, L. ; Zhong, J. ; Zhang, Y. ; Bai, S. ; Li, G. ; Yang, Y. An improving faster-RCNN with multi-attention ResNet for small target detection in intelligent autonomous transport with 6G. 2023 IEEE Trans Intell Transp Syst. 24 7717-7725
    Paper not yet in RePEc: Add citation now
    
51. Yildiz, C. ; Acikgoz, H. ; Korkmaz, D. ; Budak, U. An improved residual-based convolutional neural network for very short-term wind power forecasting. 2021 Energ Conver Manage. 228 -
    Paper not yet in RePEc: Add citation now
    
52. Yilmaz, A. ; Poli, R. Successfully and efficiently training deep multi-layer perceptrons with logistic activation function simply requires initializing the weights with an appropriate negative mean. 2022 Neural Netw. 153 87-103
    Paper not yet in RePEc: Add citation now
    
53. Yin, X. ; Zhao, X. Deep neural learning based distributed predictive control for offshore wind farm using high-fidelity LES data. 2020 IEEE Trans Ind Electron. 68 3251-3261
    Paper not yet in RePEc: Add citation now
    
54. Zhang, C. ; Shafieezadeh, A. Nested physics-informed neural network for analysis of transient flows in natural gas pipelines. 2023 Eng Appl Artif Intel. 122 -
    Paper not yet in RePEc: Add citation now
    

55. Zhang, J. ; Zhao, X. Spatiotemporal wind field prediction based on physics-informed deep learning and LIDAR measurements. 2021 Appl Energy. 288 -

56. Zhang, J. ; Zhao, X. Three-dimensional spatiotemporal wind field reconstruction based on physics-informed deep learning. 2021 Appl Energy. 300 -

57. Zhang, K. ; Tang, B. ; Deng, L. ; Liu, X. A hybrid attention improved ResNet based fault diagnosis method of wind turbines gearbox. 2021 Measurement. 179 -
    Paper not yet in RePEc: Add citation now
    
58. Zhang, W. ; Li, J. CPINNs: a coupled physics-informed neural networks for the closed-loop geothermal system. 2023 Comput Math Appl. 132 161-179
    Paper not yet in RePEc: Add citation now
    

1. Review of several key processes in wind power forecasting: Mathematical formulations, scientific problems, and logical relations. (2025). Xu, Chuanyu ; Yang, Mao ; Huang, Yutong ; Liu, Chenyu ; Dai, Bozhi.
   In: Applied Energy.
   RePEc:eee:appene:v:377:y:2025:i:pc:s0306261924020142.

   Full description at Econpapers || Download paper

2. Review of Computational Fluid Dynamics in the Design of Floating Offshore Wind Turbines. (2024). Lin, Zaibin ; Shi, Wei ; Haider, Rizwan ; Xiao, Qing ; Zhao, Haisheng.
   In: Energies.
   RePEc:gam:jeners:v:17:y:2024:i:17:p:4269-:d:1464569.

   Full description at Econpapers || Download paper

3. A review of physical and numerical modeling techniques for horizontal-axis wind turbine wakes. (2024). Estefen, Segen F ; Amiri, Mojtaba Maali ; Shadman, Milad.
   In: Renewable and Sustainable Energy Reviews.
   RePEc:eee:rensus:v:193:y:2024:i:c:s1364032124000029.

   Full description at Econpapers || Download paper

4. Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction. (2024). Wei, Zhinong ; Cheng, Lilin ; Liu, Jingxuan ; Zang, Haixiang ; Sun, Guoqiang.
   In: Energy.
   RePEc:eee:energy:v:293:y:2024:i:c:s0360544224003098.

   Full description at Econpapers || Download paper

5. A multiscale and multivariable differentiated learning for carbon price forecasting. (2024). Zhao, Xuefeng ; Chen, Linfei.
   In: Energy Economics.
   RePEc:eee:eneeco:v:131:y:2024:i:c:s0140988324000616.

   Full description at Econpapers || Download paper

6. Residual-connected physics-informed neural network for anti-noise wind field reconstruction. (2024). Zhang, Zhihao ; Tian, Runze ; Kou, Peng ; Mei, Mingyang ; Liang, Deliang.
   In: Applied Energy.
   RePEc:eee:appene:v:357:y:2024:i:c:s0306261923018032.

   Full description at Econpapers || Download paper

7. A Pragmatic Framework for Data-Driven Decision-Making Process in the Energy Sector: Insights from a Wind Farm Case Study. (2023). Chountalas, Panos T ; Konstas, Konstantinos ; Didaskalou, Eleni A ; Georgakellos, Dimitrios A.
   In: Energies.
   RePEc:gam:jeners:v:16:y:2023:i:17:p:6272-:d:1227893.

   Full description at Econpapers || Download paper

8. Near real-time wind speed forecast model with bidirectional LSTM networks. (2023). Salcedo-Sanz, Sancho ; Deo, Ravinesh C ; Prasad, Ramendra ; Raj, Nawin ; Joseph, Lionel P ; Soar, Jeffrey.
   In: Renewable Energy.
   RePEc:eee:renene:v:204:y:2023:i:c:p:39-58.

   Full description at Econpapers || Download paper

9. A novel ensemble system for short-term wind speed forecasting based on Two-stage Attention-Based Recurrent Neural Network. (2023). Zhang, Ziyuan ; Xia, Yurui ; Luo, Tianrui ; Wang, Jianzhou ; Wei, Danxiang.
   In: Renewable Energy.
   RePEc:eee:renene:v:204:y:2023:i:c:p:11-23.

   Full description at Econpapers || Download paper

10. A wind speed forecasting model based on multi-objective algorithm and interpretability learning. (2023). He, Zhaoshuang ; Yang, YI ; Guo, Xinbo ; Zhang, Ruisheng ; Li, Min ; Huang, Bingqing.
    In: Energy.
    RePEc:eee:energy:v:269:y:2023:i:c:s036054422300172x.

    Full description at Econpapers || Download paper

11. Hybrid wind speed forecasting using ICEEMDAN and transformer model with novel loss function. (2023). Kosana, Vishalteja ; Teeparthi, Kiran ; Bommidi, Bala Saibabu.
    In: Energy.
    RePEc:eee:energy:v:265:y:2023:i:c:s0360544222032698.

    Full description at Econpapers || Download paper

12. Numerical investigations of coupled aeroelastic performance of wind turbines by elastic actuator line model. (2023). Strijhak, Sergei ; Wang, Nina ; Zheng, Jiancai.
    In: Applied Energy.
    RePEc:eee:appene:v:330:y:2023:i:pb:s030626192201618x.

    Full description at Econpapers || Download paper

13. State-of-the-Art Using Bibliometric Analysis of Wind-Speed and -Power Forecasting Methods Applied in Power Systems. (2022). Martinez, Maximiliano ; Caicedo, Joaquin E ; Riquelme, Jesus ; Coria, Gustavo ; Suvire, Gaston ; Quete, Andres Romero ; Lagos, Ana.
    In: Energies.
    RePEc:gam:jeners:v:15:y:2022:i:18:p:6545-:d:909362.

    Full description at Econpapers || Download paper

14. Developing a wind power forecasting system based on deep learning with attention mechanism. (2022). Wei, Wei ; Niu, Tong ; Tian, Chaonan.
    In: Energy.
    RePEc:eee:energy:v:257:y:2022:i:c:s036054422201653x.

    Full description at Econpapers || Download paper

15. A simple approach for short-term wind speed interval prediction based on independently recurrent neural networks and error probability distribution. (2022). Gan, Zhenhao ; Xie, Yuying ; Liu, Fangjie ; Saeed, Adnan.
    In: Energy.
    RePEc:eee:energy:v:238:y:2022:i:pc:s036054422102260x.

    Full description at Econpapers || Download paper

16. Model-form uncertainty quantification in RANS simulations of wakes and power losses in wind farms. (2021). Yang, Xiang ; Ghiassi, Reza ; Abkar, Mahdi ; Eidi, Ali.
    In: Renewable Energy.
    RePEc:eee:renene:v:179:y:2021:i:c:p:2212-2223.

    Full description at Econpapers || Download paper

17. Quantifying structural uncertainties in Reynolds-averaged Navier–Stokes simulations of wind turbine wakes. (2021). Hornshoj-Moller, Simon D ; Forooghi, Pourya ; Nielsen, Peter D ; Abkar, Mahdi.
    In: Renewable Energy.
    RePEc:eee:renene:v:164:y:2021:i:c:p:1550-1558.

    Full description at Econpapers || Download paper

18. Optimizing wind farms layouts for maximum energy production using probabilistic inference: Benchmarking reveals superior computational efficiency and scalability. (2021). , Enrico ; Romero, David A ; Amon, Cristina H ; Dhoot, Aditya.
    In: Energy.
    RePEc:eee:energy:v:223:y:2021:i:c:s036054422100284x.

    Full description at Econpapers || Download paper

19. A comparison of optimizers in a unified standard for optimization on wind farm layout optimization. (2021). Hennecke, David ; Croonenbroeck, Carsten.
    In: Energy.
    RePEc:eee:energy:v:216:y:2021:i:c:s0360544220323513.

    Full description at Econpapers || Download paper

20. Atmospheric pressure gradients and Coriolis forces provide geophysical limits to power density of large wind farms. (2021). Caldeira, Ken.
    In: Applied Energy.
    RePEc:eee:appene:v:281:y:2021:i:c:s0306261920314835.

    Full description at Econpapers || Download paper

21. A design methodology for wind farm layout considering cable routing and economic benefit based on genetic algorithm and GeoSteiner. (2020). Wang, Yufei ; Wu, Yan ; Feng, Xiao ; Zhang, Shuai.
    In: Renewable Energy.
    RePEc:eee:renene:v:146:y:2020:i:c:p:687-698.

    Full description at Econpapers || Download paper

22. Optimal design of wind farms in complex terrains using computational fluid dynamics and adjoint methods. (2020). , Enrico ; Romero, David A ; Amon, Cristina H.
    In: Applied Energy.
    RePEc:eee:appene:v:261:y:2020:i:c:s0306261919321130.

    Full description at Econpapers || Download paper

23. AD/RANS Simulations of Wind Turbine Wake Flow Employing the RSM Turbulence Model: Impact of Isotropic and Anisotropic Inflow Conditions. (2019). Song, Yilei ; Shen, Wen Zhong ; Wang, Tongguang ; Zhao, Ning ; Tian, Linlin.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:21:p:4026-:d:279308.

    Full description at Econpapers || Download paper

24. Performance Enhancement of a Multiresonant Piezoelectric Energy Harvester for Low Frequency Vibrations. (2019). Lim, Yee Yan ; Padilla, Ricardo Vasquez ; Sedighi, Mohammadreza ; Novak, Jeremy Paul ; Izadgoshasb, Iman.
    In: Energies.
    RePEc:gam:jeners:v:12:y:2019:i:14:p:2770-:d:249775.

    Full description at Econpapers || Download paper

Report date: 2025-09-26 02:23:31 || Missing content? Let us know