Wind field forecasting using a novel method based on convolutional neural networks and bidirectional LSTM

References

• Abbasi MH, Taki M, Rajabi A, Li L, Zhang J. 2019. Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: a multi-stage risk constrained approach. Appl Energy. 239:1294–1307.
   Web of Science ®Google Scholar
• Altan A, Karasu S, Zio E. 2021. A new hybrid model for wind speed forecasting combining long short-term memory neural network, decomposition methods and grey wolf optimizer. Appl Soft Comput. 100:106996.
   Web of Science ®Google Scholar
• Cai H, Jia X, Feng J, Li W, Hsu Y-M, Lee J. 2020. Gaussian process regression for numerical wind speed prediction enhancement. Renew Energy. 146:2112–2123.
   Web of Science ®Google Scholar
• Cai W, Cai D, Huang S, Li M. 2019. Utterance-level end-to-end language identification using attention-based CNN-BLSTM. Paper Read at ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
   Google Scholar
• Chen J, Zeng G-Q, Zhou W, Du W, Lu K-D. 2018. Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy Convers Manage. 165:681–695.
   Web of Science ®Google Scholar
• De Freitas N, Silva M, Sakamoto MS. 2018. Wind speed forecasting: a review. Int J Eng Res Appl. 8:4–9.
   Google Scholar
• Duan J, Zuo H, Bai Y, Duan J, Chang M, Chen B. 2021. Short-term wind speed forecasting using recurrent neural networks with error correction. Energy. 217:119397.
   Web of Science ®Google Scholar
• Guo Z-h, Wu J, Lu H-y, Wang J-z. 2011. A case study on a hybrid wind speed forecasting method using BP neural network. Knowl Based Syst. 24(7):1048–1056.
   Web of Science ®Google Scholar
• Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Comput. 9(8):1735–1780.
   PubMed Web of Science ®Google Scholar
• Hosseini P. 2022. Deep-learning neural network prediction of a solar-based absorption chiller cooling system performance using waste heat. Sustain Energy Technol Assess. 53:102683.
   Google Scholar
• Hosseini P, Taheri S, Akhavan J, Razban A. 2023. Privacy-preserving federated learning: application to behind-the-meter solar photovoltaic generation forecasting. Energy Convers Manage. 283:116900.
   Web of Science ®Google Scholar
• Hu H, Wang L, Tao R. 2021. Wind speed forecasting based on variational mode decomposition and improved echo state network. Renew Energy. 164:729–751.
   Web of Science ®Google Scholar
• Ismaiel A. 2022. Wind turbine blade dynamics simulation under the effect of atmospheric turbulence. Emerg Sci J. 7(1):162–176.
   Google Scholar
• Javadi S, Maghami A, Hosseini SM. 2022. A deep learning approach based on a data-driven tool for classification and prediction of thermoelastic wave’s band structures for phononic crystals. Mech Adv Mater Struct. 29(27):6612–6625.
   Web of Science ®Google Scholar
• Jiang P, Liu Z, Niu X, Zhang L. 2021. A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting. Energy. 217:119361.
   Web of Science ®Google Scholar
• Jung J, Broadwater RP. 2014. Current status and future advances for wind speed and power forecasting. Renew Sustain Energy Rev. 31:762–777.
   Web of Science ®Google Scholar
• Karasu S, Altan A, Saraç Z, Hacioğlu R. 2017a. Estimation of fast varied wind speed based on NARX neural network by using curve fitting. Int J Energy Appl Technol. 4(3):137–146.
   Google Scholar
• Karasu S, Altan A, Saraç Z, Hacıoğlu R. 2017b. Estimation of wind speed by using regression learners with different filtering methods. Paper read at 1st international conference on energy systems engineering. Karabuk, Turkey.
   Google Scholar
• Kavasseri RG, Seetharaman K. 2009. Day-ahead wind speed forecasting using f-ARIMA models. Renew Energy. 34(5):1388–1393.
   Web of Science ®Google Scholar
• Kavousi-Fard A, Mohammadi M, Al-Sumaiti A. 2021. Effective strategies of flexibility in modern distribution systems: reconfiguration, renewable sources and plug-in electric vehicles. In: Alhelou HH, Heydarian-Forushani E, Siano P, editors. Flexibility in electric power distribution networks. Boca Raton: CRC Press; p. 95–119.
   Google Scholar
• LeCun Y, Bottou L, Bengio Y, Haffner P. 1998. Gradient-based learning applied to document recognition. Proc IEEE. 86(11):2278–2324.
   Web of Science ®Google Scholar
• Lei M, Mohammadi M. 2021. Hybrid machine learning based energy policy and management in the renewable-based microgrids considering hybrid electric vehicle charging demand. Int J Electr Power Energy Syst. 128:106702.
   Web of Science ®Google Scholar
• Lei M, Shiyan L, Chuanwen J, Hongling L, Yan Z. 2009. A review on the forecasting of wind speed and generated power. Renew Sustain Energy Rev. 13(4):915–920.
   Web of Science ®Google Scholar
• Liu M, Cao Z, Zhang J, Wang L, Huang C, Luo X. 2020. Short-term wind speed forecasting based on the Jaya-SVM model. Int J Electr Power Energy Syst. 121:106056.
   Web of Science ®Google Scholar
• Liu Z, Jiang P, Zhang L, Niu X. 2020. A combined forecasting model for time series: application to short-term wind speed forecasting. Appl Energy. 259:114137.
   Web of Science ®Google Scholar
• Ma R, Karimzadeh M, Ghabussi A, Zandi Y, Baharom S, Selmi A, Maureira-Carsalade N. 2021. Assessment of composite beam performance using GWO–ELM metaheuristic algorithm. Eng Comput. 38(Suppl 3):2083–2099.
   Web of Science ®Google Scholar
• Ma X, Foong LK, Morasaei A, Ghabussi A, Lyu Z. 2020. Swarm-based hybridizations of neural network for predicting the concrete strength. Smart Struct Syst Int J. 26(2):241–251.
   Web of Science ®Google Scholar
• Memarzadeh G, Keynia F. 2020. A new short-term wind speed forecasting method based on fine-tuned LSTM neural network and optimal input sets. Energy Convers Manage. 213:112824.
   Web of Science ®Google Scholar
• Mobtahej M, Esapour K, Tajalli SZ, Mohammadi M. 2021. Effective demand response and GANs for optimal constraint unit commitment in solar-tidal based microgrids. London: IET Renewable Power Generation; p. 1–11.
   Google Scholar
• Mohammadi M, Kavousi-Fard A, Dabbaghjamanesh M, Farughian A, Khosravi A. 2021. Effective management of energy internet in renewable hybrid microgrids: a secured data driven resilient architecture. IEEE Trans Ind Inf. 18(3):1896–1904.
   Web of Science ®Google Scholar
• Mohammadi M, Kavousi-Fard A, Dehghani M, Karimi M, Loia V, Alhelou HH, Siano P. 2022. Reinforcing data integrity in renewable hybrid AC-DC microgrids from social-economic perspectives. New York, NY: ACM Transactions on Sensor Networks; p. 1–19.
   Google Scholar
• Momeni A, Piratla K. 2019. A novel cyber-monitoring based asset management scheme for water distribution networks through fine-tuning genetic algorithm parameters. Paper Read at ISTT No Dig 2019 conference.
   Google Scholar
• Morasaei A, Ghabussi A, Aghlmand S, Yazdani M, Baharom S, Assilzadeh H. 2021. Simulation of steel–concrete composite floor system behavior at elevated temperatures via multi-hybrid metaheuristic framework. Eng Comput. 38(Suppl 3):2567–2582.
   Web of Science ®Google Scholar
• Movahednia M, Karimi H, Jadid S. 2020. Optimal hierarchical energy management scheme for networked microgrids considering uncertainties, demand response, and adjustable power. IET Gener Transm Distrib. 14(20):4352–4362.
   Web of Science ®Google Scholar
• Neshat M, Majidi Nezhad M, Abbasnejad E, Mirjalili SA, Tjernberg LB, Garcia DA, Alexander B, Wagner M. 2021. A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm. Energy Convers Manage. 1–25.
   Web of Science ®Google Scholar
• Pavlova A, Myslenkov S, Arkhipkin V, Surkova G. 2021. Storm surges and storm wind waves in the Caspian Sea in the present and future climate. Natural Hazards and Earth System Sciences Discussions, 1–31.
   Google Scholar
• Pour ES, Jafari H, Lashgari A, Rabiee E, Ahmadisharaf A. 2022. Cryptocurrency price prediction with neural networks of LSTM and Bayesian optimization. Eur J Bus Manag Res. 7(2):20–27.
   Google Scholar
• Ramprasath M, Anand MV, Hariharan S. 2018. Image classification using convolutional neural networks. Int J Pure Appl Math. 119(17):1307–1319.
   Google Scholar
• Saxena BK, Mishra S, Rao KVS. 2021. Offshore wind speed forecasting at different heights by using ensemble empirical mode decomposition and deep learning models. Appl Ocean Res. 117:102937.
   Web of Science ®Google Scholar
• Sideratos G, Hatziargyriou ND. 2020. A distributed memory RBF-based model for variable generation forecasting. Int J Electr Power Energy Syst. 120:106041.
   Web of Science ®Google Scholar
• Tian C, Hao Y, Hu J. 2018. A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization. Appl Energy. 231:301–319.
   Web of Science ®Google Scholar
• Werbos PJ. 1990. Backpropagation through time: what it does and how to do it. Proc IEEE. 78(10):1550–1560.
   Web of Science ®Google Scholar
• Zhang, L., J. Wang, X. Niu, and Z. Liu. 2021. Ensemble wind speed forecasting with multi-objective Archimedes optimization algorithm and sub-model selection. Appl Energy 301:117449.
   Web of Science ®Google Scholar