A Comprehensive Review on Deep Learning Algorithms for Wind Power Prediction

##plugins.themes.academic_pro.article.main##

Geetika Sharma
Madan Lal
Kanwal Preet Singh Attwal

Abstract

In recent years, various energy crisis and environmental considerations have prompted the use of renewable energy resources. Renewable energy resources like solar, wind, hydro, biomass, etc. have been a continuous source of clean energy. Wind energy is one of the renewable energy resources that has been widely used all over the world. The wind power is mainly dependent on wind speed which is a random variable and its unpredictable behavior creates various challenges for wind farm operators like energy dispatching and system scheduling. Hence, predicting wind power energy becomes crucial. This        has led to the development of various forecasting models in the recent decades. The most commonly used deep learning algorithms for wind power prediction are- RNN (Recurrent Neural Network), LSTM (Long Short- Term Memory) and CNN (Convolutional Neural Network). This paper presents the working of these algorithms and provides a timeline review of the research papers that used these algorithms for wind power prediction.

##plugins.themes.academic_pro.article.details##

How to Cite
Geetika Sharma, Madan Lal, & Kanwal Preet Singh Attwal. (2022). A Comprehensive Review on Deep Learning Algorithms for Wind Power Prediction. International Journal of Next-Generation Computing, 13(4). https://doi.org/10.47164/ijngc.v13i4.631

References

  1. Alkhayat, G. and Mehmood, R. 2021. A review and taxonomy of wind and solar energy forecasting methods based on deep learning. Energy and AI 4, 100060. DOI: https://doi.org/10.1016/j.egyai.2021.100060
  2. Bai, L., Crisostomi, E., Raugi, M., and Tucci, M. 2019. Wind power forecast using wind forecasts at different altitudes in convolutional neural networks. In 2019 IEEE Power & Energy Society General Meeting (PESGM). IEEE, 1–5. DOI: https://doi.org/10.1109/PESGM40551.2019.8973938
  3. Bazionis, I. K. and Georgilakis, P. S. 2021. Review of deterministic and probabilistic wind power forecasting: Models, methods, and future research. Electricity 2, 1, 13–47. DOI: https://doi.org/10.3390/electricity2010002
  4. Cali, U. and Sharma, V. 2019. Short-term wind power forecasting using long-short term memory based recurrent neural network model and variable selection. International Journal of Smart Grid and Clean Energy 8, 2, 103–110. DOI: https://doi.org/10.12720/sgce.8.2.103-110
  5. Chen, G., Shan, J., Li, D. Y., Wang, C., Li, C., Zhou, Z., Wang, X., Li, Z., and Hao, J. J. 2019. Research on wind power prediction method based on convolutional neural network and genetic algorithm. 2019 IEEE Innovative Smart Grid Technologies-Asia (ISGT Asia), 3573–3578. DOI: https://doi.org/10.1109/ISGT-Asia.2019.8880918
  6. Cheng, L., Zang, H., Xu, Y., Wei, Z., and Sun, G. 2021. Augmented convolutional network for wind power prediction: A new recurrent architecture design with spatial-temporal image inputs. IEEE Transactions on Industrial Informatics 17, 10, 6981–6993. DOI: https://doi.org/10.1109/TII.2021.3063530
  7. Devi, A. S., Maragatham, G., Boopathi, K., and Rangaraj, A. 2020. Hourly day- ahead wind power forecasting with the eemd-cso-lstm-efg deep learning technique. Soft Computing 24, 16, 12391–12411. DOI: https://doi.org/10.1007/s00500-020-04680-7
  8. Dolara, A., Gandelli, A., Grimaccia, F., Leva, S., and Mussetta, M. 2017. Weather- based machine learning technique for day-ahead wind power forecasting. In 2017 IEEE 6th international conference on renewable energy research and applications (ICRERA). IEEE, 206–209. DOI: https://doi.org/10.1109/ICRERA.2017.8191267
  9. Dong, D., Sheng, Z., and Yang, T. 2018. Wind power prediction based on recurrent neural network with long short-term memory units. In 2018 International Conference on Renew- able Energy and Power Engineering (REPE). IEEE, 34–38. DOI: https://doi.org/10.1109/REPE.2018.8657666
  10. Du, M. 2019. Improving lstm neural networks for better short-term wind power predictions. In 2019 IEEE 2nd International Conference on Renewable Energy and Power Engineering (REPE). IEEE, 105–109. DOI: https://doi.org/10.1109/REPE48501.2019.9025143
  11. Fu, Y., Hu, W., Tang, M., Yu, R., and Liu, B. 2018. Multi-step ahead wind power forecasting based on recurrent neural networks. In 2018 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 217–222. DOI: https://doi.org/10.1109/APPEEC.2018.8566471
  12. Guangxu, P., Haijing, Z., Wenjie, J., Weijin, Y., Chenglong, Q., Liwei, P., Yuan, S., and Ruiqi, W. 2020. A prediction method for ultra short-term wind power prediction basing on long short-term memory network and extreme learning machine. In 2020 Chinese Automation Congress (CAC). IEEE, 7608–7612. DOI: https://doi.org/10.1109/CAC51589.2020.9327895
  13. Gupta, R., Kumar, R., and Bansal, A. K. 2011. Selection of input variables for the prediction of wind speed in wind farms based on genetic algorithm. Wind Engineering 35, 6, 649–660. DOI: https://doi.org/10.1260/0309-524X.35.6.649
  14. Hanifi, S., Liu, X., Lin, Z., and Lotfian, S. 2020. A critical review of wind power forecasting methods—past, present and future. Energies 13, 15, 3764. DOI: https://doi.org/10.3390/en13153764
  15. Huang, H. and Yaming, L. 2020. Short-term tie-line power prediction based on cnn-lstm. In 2020 IEEE 4th Conference on Energy Internet and Energy System Integration (EI2). IEEE, 4118–4122. DOI: https://doi.org/10.1109/EI250167.2020.9346998
  16. Jørgensen, K. L. and Shaker, H. R. 2020. Wind power forecasting using machine learning: State of the art, trends and challenges. In 2020 IEEE 8th International Conference on Smart Energy Grid Engineering (SEGE). IEEE, 44–50. DOI: https://doi.org/10.1109/SEGE49949.2020.9181870
  17. Li, A. and Cheng, L. 2019. Research on a forecasting model of wind power based on recurrent neural network with long short-term memory. In 2019 22nd International Conference on Electrical Machines and Systems (ICEMS). IEEE, 1–4. DOI: https://doi.org/10.1109/ICEMS.2019.8922153
  18. Li, J., Geng, D., Zhang, P., Meng, X., Liang, Z., and Fan, G. 2019. Ultra-short term wind power forecasting based on lstm neural network. In 2019 IEEE 3rd International Electrical and Energy Conference (CIEEC). IEEE, 1815–1818. DOI: https://doi.org/10.1109/CIEEC47146.2019.CIEEC-2019625
  19. Liu, B., Zhao, S., Yu, X., Zhang, L., and Wang, Q. 2020. A novel deep learning approach for wind power forecasting based on wd-lstm model. Energies 13, 18, 4964. DOI: https://doi.org/10.3390/en13184964
  20. Liu, H., Chen, D., Lin, F., and Wan, Z. 2021. Wind power short-term forecasting based on lstm neural network with dragonfly algorithm. In Journal of Physics: Conference Series. Vol. 1748. IOP Publishing, 032015. DOI: https://doi.org/10.1088/1742-6596/1748/3/032015
  21. Liu, X., Yang, L., and Zhang, Z. 2021. Short-term multi-step ahead wind power predictions based on a novel deep convolutional recurrent network method. IEEE Transactions on Sustainable Energy 12, 3, 1820–1833. DOI: https://doi.org/10.1109/TSTE.2021.3067436
  22. Liu, X., Zhou, J., and Qian, H. 2021. Short-term wind power forecasting by stacked re- current neural networks with parametric sine activation function. Electric Power Systems Research 192, 107011. DOI: https://doi.org/10.1016/j.epsr.2020.107011
  23. Lydia, M. and Kumar, S. S. 2010. A comprehensive overview on wind power forecasting. In 2010 Conference proceedings IPEC. IEEE, 268–273. DOI: https://doi.org/10.1109/IPECON.2010.5697118
  24. Paterakis, N. G., Mocanu, E., Gibescu, M., Stappers, B., and van Alst, W. 2017. Deep learning versus traditional machine learning methods for aggregated energy demand prediction. In 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe). IEEE, 1–6. DOI: https://doi.org/10.1109/ISGTEurope.2017.8260289
  25. Saini, V. K., Kumar, R., Mathur, A., and Saxena, A. 2020. Short term forecasting based on hourly wind speed data using deep learning algorithms. In 2020 3rd International Conference on Emerging Technologies in Computer Engineering: Machine Learning and Internet of Things (ICETCE). IEEE, 1–6. DOI: https://doi.org/10.1109/ICETCE48199.2020.9091757
  26. Shabbir, N., Ku¨tt, L., Jawad, M., Amadiahanger, R., Iqbal, M. N., and Rosin, A. 2019. Wind energy forecasting using recurrent neural networks. In 2019 Big Data, Knowledge and Control Systems Engineering (BdKCSE). IEEE, 1–5. DOI: https://doi.org/10.1109/BdKCSE48644.2019.9010593
  27. Shahid, F., Zameer, A., Mehmood, A., and Raja, M. A. Z. 2020. A novel wavenets long short term memory paradigm for wind power prediction. Applied Energy 269, 115098. DOI: https://doi.org/10.1016/j.apenergy.2020.115098
  28. Shahid, F., Zameer, A., and Muneeb, M. 2021. A novel genetic lstm model for wind power forecast. Energy 223, 120069. DOI: https://doi.org/10.1016/j.energy.2021.120069
  29. Shi, X., Lei, X., Huang, Q., Huang, S., Ren, K., and Hu, Y. 2018. Hourly day-ahead wind power prediction using the hybrid model of variational model decomposition and long short-term memory. Energies 11, 11, 3227. DOI: https://doi.org/10.3390/en11113227
  30. Shi, Z., Liang, H., and Dinavahi, V. 2017. Direct interval forecast of uncertain wind power based on recurrent neural networks. IEEE Transactions on Sustainable Energy 9, 3, 1177– 1187. DOI: https://doi.org/10.1109/TSTE.2017.2774195
  31. Solas, M., Cepeda, N., and Viegas, J. L. 2019a. Convolutional neural network for short-term wind power forecasting. In 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, 1–5.
  32. Solas, M., Cepeda, N., and Viegas, J. L. 2019b. Convolutional neural network for short-term wind power forecasting. In 2019 IEEE PES Innovative Smart Grid Technologies Europe (ISGT-Europe). IEEE, 1–5. DOI: https://doi.org/10.1109/ISGTEurope.2019.8905432
  33. Soman, S. S., Zareipour, H., Malik, O., and Mandal, P. 2010. A review of wind power and wind speed forecasting methods with different time horizons. In North American power symposium 2010. IEEE, 1–8. DOI: https://doi.org/10.1109/NAPS.2010.5619586
  34. Son, N., Yang, S., and Na, J. 2019. Hybrid forecasting model for short-term wind power prediction using modified long short-term memory. Energies 12, 20, 3901. DOI: https://doi.org/10.3390/en12203901
  35. Toubeau, J.-F., Dapoz, P.-D., Bottieau, J., Wautier, A., De Greve, Z., and Valle´e, F. 2021. Recalibration of recurrent neural networks for short-term wind power forecasting. Electric Power Systems Research 190, 106639. DOI: https://doi.org/10.1016/j.epsr.2020.106639
  36. Vaitheeswaran, S. S. and Ventrapragada, V. R. 2019. Wind power pattern prediction in time series measuremnt data for wind energy prediction modelling using lstm-ga networks. In 2019 10th International Conference on Computing, Communication and Networking Technologies (ICCCNT). IEEE, 1–5. DOI: https://doi.org/10.1109/ICCCNT45670.2019.8944827
  37. Wang, S., Li, B., Li, G., Yao, B., and Wu, J. 2021. Short-term wind power prediction based on multidimensional data cleaning and feature reconfiguration. Applied Energy 292, 116851. DOI: https://doi.org/10.1016/j.apenergy.2021.116851
  38. Xia, M., Shao, H., Ma, X., and de Silva, C. W. 2021. A stacked gru-rnn-based approach for predicting renewable energy and electricity load for smart grid operation. IEEE Trans- actions on Industrial Informatics 17, 10, 7050–7059. DOI: https://doi.org/10.1109/TII.2021.3056867
  39. Xiaosheng, P., Kai, C., Bo, W., Jianfeng, C., Qiyou, X., Fan, Y., and Wenze, L. 2020. Short-term wind power prediction based on wavelet transform and convolutional neural networks. In 2020 IEEE/IAS Industrial and Commercial Power System Asia (I&CPS Asia). IEEE, 1244–1250. DOI: https://doi.org/10.1109/ICPSAsia48933.2020.9208382
  40. Xiaoyun, Q., Xiaoning, K., Chao, Z., Shuai, J., and Xiuda, M. 2016. Short-term prediction of wind power based on deep long short-term memory. In 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 1148–1152.
  41. Yildiz, C., Acikgoz, H., Korkmaz, D., and Budak, U. 2021. An improved residual-based convolutional neural network for very short-term wind power forecasting. Energy Conver- sion and Management 228, 113731. DOI: https://doi.org/10.1016/j.enconman.2020.113731
  42. Zhang, J., Jiang, X., Chen, X., Li, X., Guo, D., and Cui, L. 2019. Wind power generation prediction based on lstm. In Proceedings of the 2019 4th International Conference on Mathematics and Artificial Intelligence. 85–89. DOI: https://doi.org/10.1145/3325730.3325735
  43. Zhao, Y. and Jia, L. 2020. A new hybrid forecasting architecture of wind power based on a newly developed temporal convolutional networks. In 2020 IEEE 9th Data Driven Control and Learning Systems Conference (DDCLS). IEEE, 839–844. DOI: https://doi.org/10.1109/DDCLS49620.2020.9275243
  44. Zhu, A., Li, X., Mo, Z., and Wu, R. 2017. Wind power prediction based on a convolu- tional neural network. In 2017 International Conference on Circuits, Devices and Systems (ICCDS). IEEE, 131–135. DOI: https://doi.org/10.1109/ICCDS.2017.8120465
  45. Zhu, R., Liao, W., and Wang, Y. 2020. Short-term prediction for wind power based on temporal convolutional network. Energy Reports 6, 424–429. DOI: https://doi.org/10.1016/j.egyr.2020.11.219