Estimation of wind turbine output power using soft computing models

References

• Akbiyik, H., and H. Yavuz. 2021. Artificial neural network application for aerodynamics of an airfoil equipped with plasma actuators. Journal of Applied Fluid Mechanics 14 (4):1165-81. https://doi.org/10.47176/jafm.14.04.32133
   Google Scholar
• Arora, I., J. Gambhir, and T. Kaur. 2020. Data normalisation-based solar irradiance forecasting using artificial neural networks. Arabian Journal for Science and Engineering 46 (2):1333–43. doi:10.1007/s13369-020-05140-y.
   Web of Science ®Google Scholar
• Asghar, A. B., and X. Liu. 2018. Adaptive neuro-fuzzy algorithm to estimate effective wind speed and optimal rotor speed for variable-speed wind turbine. Neurocomputing 272:495–504. doi:10.1016/j.neucom.2017.07.022.
   Web of Science ®Google Scholar
• Başakın, E. E., Ö. Ekmekcioğlu, H. Çıtakoğlu, and M. Özger. 2021. A new insight to the wind speed forecasting: Robust multi-stage ensemble soft computing approach based on pre-processing uncertainty assessment. Neural Computing and Applications 34 (1):783–812. doi:10.1007/s00521-021-06424-6.
   Web of Science ®Google Scholar
• Benmouiza, K., and A. Cheknane. 2018. Clustered ANFIS network using Fuzzy C-means, subtractive clustering, and grid partitioning for hourly solar radiation forecasting. Theoretical and Applied Climatology 137 (1–2):31–43. doi:10.1007/s00704-018-2576-4.
   Web of Science ®Google Scholar
• Bilgili, M., and B. Sahin. 2010. Comparative analysis of regression and artificial neural network models for wind speed prediction. Meteorology and Atmospheric Physics 109 (1–2):61–72. doi:10.1007/s00703-010-0093-9.
   Web of Science ®Google Scholar
• Bilgili, M., B. Şahin, and A. Kahraman. 2004. Wind energy potential in Antakya and iskenderun regions, Turkey. Renewable Energy 29 (10):1733–45. doi:10.1016/j.renene.2003.10.003.
   Web of Science ®Google Scholar
• Bilgili, M., S. Tumse, M. Tontu, and B. Sahin. 2021. Effect of growth in turbine size on rotor aerodynamic performance of modern commercial large-scale wind turbines. Arabian Journal for Science and Engineering 46 (8):7185–95. doi:10.1007/s13369-021-05364-6.
   Web of Science ®Google Scholar
• Cacciola, M., G. Megali, D. Pellicanó, and F. C. Morabito. 2011. Elman neural networks for characterizing voids in welded strips: A study. Neural Computing and Applications 21 (5):869–75. doi:10.1007/s00521-011-0609-3.
   Web of Science ®Google Scholar
• Catalao, J. P., H. M. Pousinho, and V. M. Mendes (2011). Hybrid Wavelet-PSO-ANFIS Approach for Short-Term Wind power Forecasting in Portugal. 2011 IEEE Power and Energy Society General Meeting. 10.1109/pes.2011.6038969
   Google Scholar
• Chandra, R., and M. Zhang. 2012. Cooperative coevolution of Elman recurrent neural networks for chaotic time series prediction. Neurocomputing 86:116–23. doi:10.1016/j.neucom.2012.01.014.
   Web of Science ®Google Scholar
• Chang, W.-Y. 2014. A literature review of wind forecasting methods. Journal of Power and Energy Engineering 2 (4):161–68. doi:10.4236/jpee.2014.24023.
   Google Scholar
• Chen, W.-H., J.-S. Wang, M.-H. Chang, J. K. Mutuku, and A. T. Hoang. 2021. Efficiency improvement of a vertical-axis wind turbine using a deflector optimized by Taguchi Approach with modified additive method. Energy Conversion and Management 245:114609. doi:10.1016/j.enconman.2021.114609.
   Web of Science ®Google Scholar
• Chen, M.-R., G.-Q. Zeng, K.-D. Lu, and J. Weng. 2019. A two-layer nonlinear combination method for short-term wind speed prediction based on Elm, Enn, and LSTM. IEEE Internet of Things Journal 6 (4):6997–7010. doi:10.1109/jiot.2019.2913176.
   Web of Science ®Google Scholar
• Chen, J., G.-Q. Zeng, W. Zhou, W. Du, and K.-D. Lu. 2018. Wind speed forecasting using nonlinear-learning ensemble of deep learning time series prediction and extremal optimization. Energy Conversion and Management 165:681–95. doi:10.1016/j.enconman.2018.03.098.
   Web of Science ®Google Scholar
• Citakoglu, H. 2021. Comparison of multiple learning artificial intelligence models for estimation of long-term monthly temperatures in Turkey. Arabian Journal of Geosciences 14(20):2131. https://doi.org/10.1007/s12517-021-08484-3
   Google Scholar
• Costa, A., A. Crespo, J. Navarro, G. Lizcano, H. Madsen, and E. Feitosa. 2008. A review on the young history of the wind power short-term prediction. Renewable and Sustainable Energy Reviews 12 (6):1725–44. doi:10.1016/j.rser.2007.01.015.
   Web of Science ®Google Scholar
• Deng, Y.-C., X.-H. Tang, Z.-Y. Zhou, Y. Yang, and F. Niu. 2021. Application of machine learning algorithms in wind power: A review. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects 1–22. doi:10.1080/15567036.2020.1869867.
   Google Scholar
• Deshmukh, M. K., and C. B. Moorthy. 2010. Application of genetic algorithm to neural network model for estimation of wind power potential. Journal of Engineering, Science and Management Education 2:50–59.
   Google Scholar
• Dong, D., Z. Sheng, and T. Yang (2018). Wind power prediction based on recurrent neural network with long short-term memory units. 2018 International Conference on Renewable Energy and Power Engineering (REPE), Toronto Canada . (pp. 34-38). doi: 10.1109/repe.2018.8657666.
   Google Scholar
• Ekanayake, P., A. T. Peiris, J. M. Jayasinghe, and U. Rathnayake. 2021. Development of wind power prediction models for Pawan Danavi wind farm in Sri Lanka. Mathematical Problems in Engineering 2021, 1–13. doi:10.1155/2021/4893713.
   Google Scholar
• Elman, J. L. 1990. Finding Structure in Time. Cognitive Science 14 (2):179–211. doi:10.1207/s15516709cog1402_1.
   Web of Science ®Google Scholar
• Gomes, P., and R. Castro. 2012. Wind speed and wind power forecasting using statistical models: Autoregressive moving average (ARMA) and artificial neural networks (ANN). International Journal of Sustainable Energy Development 1 (2):41–50. doi:10.20533/ijsed.2046.3707.2012.0007.
   Google Scholar
• Graves, A., M. Liwicki, S. Fernandez, R. Bertolami, H. Bunke, and J. Schmidhuber. 2009. A novel connectionist system for unconstrained handwriting recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence 31 (5):855–68. doi:10.1109/tpami.2008.137.
   PubMed Web of Science ®Google Scholar
• Güzelel, Y. E., U. Olmuş, K. N. Çerçi, and O. Büyükalaca. 2021. Comprehensive modelling of rotary desiccant wheel with different multiple regression and machine learning methods for balanced flow. Applied Thermal Engineering 199:117544. doi:10.1016/j.applthermaleng.2021.117544.
   Web of Science ®Google Scholar
• Hagan, M. T., H. B. Demuth, and M. H. Beale. 1996. Neural Network Design. Boston: PWS Publishing.
   Google Scholar
• Han, S., Y.-H. Qiao, J. Yan, Y.-Q. Liu, L. Li, and Z. Wang. 2019. Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network. Applied Energy 239:181–91. doi:10.1016/j.apenergy.2019.01.193.
   Web of Science ®Google Scholar
• Hoang, A. T., V. V. Pham, and X. P. Nguyen. 2021. Integrating renewable sources into energy system for smart city as a sagacious strategy towards clean and sustainable process. Journal of Cleaner Production 305:127161. doi:10.1016/j.jclepro.2021.127161.
   Web of Science ®Google Scholar
• Huang, Y., S. Liu, and L. Yang. 2018. Wind speed forecasting method using EEMD and the combination forecasting method based on GPR and LSTM. Sustainability 10 (10):3693. doi:10.3390/su10103693.
   Web of Science ®Google Scholar
• Ilhan, A., S. Tumse, M. O. Tasci, M. Bilgili, and B. Sahin. 2022. Particle image velocimetry investigation of the flow for the curved type wind turbine shroud. Journal of Applied Fluid Mechanics 15 (2):373–85. doi:10.47176/jafm.15.02.33004.
   Web of Science ®Google Scholar
• Jang, J.-S. R. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE Transactions on Systems, Man, and Cybernetics 23 (3):665–85. doi:10.1109/21.256541.
   Web of Science ®Google Scholar
• Kassa, Y., J. H. Zhang, D. H. Zheng, and D. Wei. (2016). Short term wind power prediction using ANFIS. 2016 IEEE International Conference on Power and Renewable Energy (ICPRE) , Shanghai, China. (pp. 383-393). doi:10.1109/icpre.2016.7871238.
   Google Scholar
• Kavasseri, R. G., and K. Seetharaman. 2009. Day-ahead wind speed forecasting using F-Arima models. Renewable Energy 34 (5):1388–93. doi:10.1016/j.renene.2008.09.006.
   Web of Science ®Google Scholar
• Khosravi, A., R. N. N. Koury, L. Machado, and J. J. G. Pabon. 2018. Prediction of hourly solar radiation in Abu Musa Island using machine learning algorithms. Journal of Cleaner Production 176:63–75. doi:10.1016/j.jclepro.2017.12.065.
   Web of Science ®Google Scholar
• Li, L., M. Wang, F. Zhu, and C. Wang (2009) Wind power forecasting based on time series and neural network. Proceedings of the Second Symposium Intemational Computer Science, Huangshan, P. R. China. (pp. 293–97).
   Google Scholar
• Liang, S., L. Nguyen, and F. Jin (2018). A multi-variable stacked long-short term memory network for wind speed forecasting. 2018 IEEE International Conference on Big Data (Big Data), Seattle, WA, USA. (pp. 4561-4564). doi:10.1109/bigdata.2018.8622332.
   Google Scholar
• Liu, H., C. Chen, H.-Q. Tian, and Y.-F. Li. 2012. A hybrid model for wind speed prediction using empirical mode decomposition and artificial neural networks. Renewable Energy 48:545–56. doi:10.1016/j.renene.2012.06.012.
   Web of Science ®Google Scholar
• Liu, H., H.-Q. Tian, and Y.-F. Li. 2015. An EMD-recursive ARIMA method to predict wind speed for railway strong wind warning system. Journal of Wind Engineering and Industrial Aerodynamics 141:27–38. doi:10.1016/j.jweia.2015.02.004.
   Web of Science ®Google Scholar
• López, E., C. Valle, H. Allende, E. Gil, and H. Madsen. 2018. Wind power forecasting based on Echo state networks and long short-term memory. Energies 11 (3):526. doi:10.3390/en11030526.
   Web of Science ®Google Scholar
• Marndi, A., G. K. Patra, and K. C. Gouda. 2020. Short-term forecasting of wind speed using time division ensemble of hierarchical deep neural networks. Bulletin of Atmospheric Science and Technology 1 (1):91–108. doi:10.1007/s42865-020-00009-2.
   Google Scholar
• Mirrashid, M., and H. Naderpour. 2021b. Innovative Computational Intelligence-based model for vulnerability assessment of RC frames subject to seismic sequence. Journal of Structural Engineering 147 (3):04020350. doi:10.1061/(asce)st.1943-541x.0002921.
   Web of Science ®Google Scholar
• Mohandes, M., S. Rehman, and S. M. Rahman. 2011. Estimation of wind speed profile using adaptive neuro-fuzzy inference system (ANFIS). Applied Energy 88 (11):4024–32. doi:10.1016/j.apenergy.2011.04.015.
   Web of Science ®Google Scholar
• Moschopoulos, M., G. N. Rossopoulos, and C. I. Papadopoulos. 2021. Journal bearing performance prediction using machine learning and octave-band signal analysis of Sound and vibration measurements. Polish Maritime Research 28 (3):137–49. doi:10.2478/pomr-2021-0041.
   Web of Science ®Google Scholar
• Moustris, K. P., D. Zafirakis, K. A. Kavvadias, and J. K. Kaldellis (2016). Wind power forecasting using historical data and artificial neural networks modeling. Mediterranean Conference on Power Generation, Transmission, Distribution and Energy Conversion (MedPower 2016), Belgrade. (pp. 1–6).doi:10.1049/cp.2016.1094.
   Google Scholar
• Naderpour, H., and M. Mirrashid. 2020. Proposed soft computing models for moment capacity prediction of reinforced concrete columns. Soft Computing 24 (15):11715–29. doi:10.1007/s00500-019-04634-8.
   Web of Science ®Google Scholar
• Naderpour, H., M. Mirrashid, and P. Parsa. 2021. Failure mode prediction of reinforced concrete columns using machine learning methods. Engineering Structures 248:113263. doi:10.1016/j.engstruct.2021.113263.
   Web of Science ®Google Scholar
• Pai, S., and S. A. Soman (2017). Forecasting global horizontal solar irradiance: A case study based on Indian geography. 2017 7th International Conference on Power Systems (ICPS), India. (pp. 247–252). doi:10.1109/icpes.2017.8387301.
   Google Scholar
• Peiris, A. T., J. Jayasinghe, and U. Rathnayake. 2021. Forecasting wind power generation using artificial neural network: “Pawan Danawi”—a case study from Sri Lanka. Journal of Electrical and Computer Engineering 2021, 1–10. doi:10.1155/2021/5577547.
   Google Scholar
• Pinson, P., H. A. Nielsen, H. Madsen, and G. Kariniotakis. 2009. Skill forecasting from ensemble predictions of Wind Power. Applied Energy 86 (7–8):1326–34. doi:10.1016/j.apenergy.2008.10.009.
   Web of Science ®Google Scholar
• Sahin, B., M. Bilgili, and H. Akilli. 2005. The wind power potential of the Eastern Mediterranean region of Turkey. Journal of Wind Engineering and Industrial Aerodynamics 93 (2):171–83. doi:10.1016/j.jweia.2004.11.005.
   Web of Science ®Google Scholar
• Shao, H., and X. Deng. 2016. Short-term wind power forecasting using model structure selection and data fusion techniques. International Journal of Electrical Power & Energy Systems 83:79–86. doi:10.1016/j.ijepes.2016.03.059.
   Web of Science ®Google Scholar
• Shi, X., X. Lei, Q. Huang, S. Huang, K. Ren, and Y. Hu. 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:10.3390/en11113227.
   Web of Science ®Google Scholar
• Singh, S., T. S. Bhatti, and D. P. Kothari. 2007. Wind power estimation using artificial neural network. Journal of Energy Engineering 133 (1):46–52. doi:10.1061/(asce)0733-9402(2007)133:1(46).
   Web of Science ®Google Scholar
• Tuan Hoang, A., S. Nižetić, H. Chyuan Ong, W. Tarelko, V. Viet Pham, T. Hieu Le, M. Quang Chau, and X. Phuong Nguyen. 2021. A review on application of artificial neural network (ANN) for performance and emission characteristics of diesel engine fueled with biodiesel-based fuels. Sustainable Energy Technologies and Assessments 47:101416. doi:10.1016/j.seta.2021.101416.
   Web of Science ®Google Scholar
• Tumse, S., M. Bilgili, and B. Şahin. 2022. Estimation of aerodynamic coefficients of a non-slender delta wing under ground effect using artificial intelligence techniques. Neural Computing and Applications. doi:10.1007/s00521-022-07013-x.
   Google Scholar
• Ucar, A., and F. Balo. 2009. Investigation of wind characteristics and assessment of wind-generation potentiality in Uludağ-Bursa, Turkey. Applied Energy 86 (3):333–39. doi:10.1016/j.apenergy.2008.05.001.
   Web of Science ®Google Scholar
• Wang, J., and Y. Li. 2018. Multi-step ahead wind speed prediction based on optimal feature extraction, Long short term memory neural network and error correction strategy. Applied Energy 230:429–43. doi:10.1016/j.apenergy.2018.08.114.
   Web of Science ®Google Scholar
• Wang, J., W. Zhang, Y. Li, J. Wang, and Z. Dang. 2014. Forecasting wind speed using empirical mode decomposition and Elman Neural Network. Applied Soft Computing 23:452–59. doi:10.1016/j.asoc.2014.06.027.
   Web of Science ®Google Scholar
• Wu, W., K. Chen, Y. Qiao, and Z. Lu (2016). Probabilistic short-term wind power forecasting based on Deep Neural Networks. 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS), Beijing, China. (pp. 1–8). doi:10.1109/pmaps.2016.7764155.
   Google Scholar
• Xiaoyun, Q., K. Xiaoning, Z. Chao, J. Shuai, and M. Xiuda. (2016). Short-term prediction of wind power based on deep long short-term memory. 2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Xi'an.(pp. 1148–1152). doi: 10.1109/appeec.2016.7779672.
   Google Scholar
• Xydis, G., C. Koroneos, and M. Loizidou. 2009. Exergy analysis in a wind speed prognostic model as a wind farm sitting selection tool: A case study in southern Greece. Applied Energy 86 (11):2411–20. doi:10.1016/j.apenergy.2009.03.017.
   Web of Science ®Google Scholar
• Zagan, R., I. Paprocka, M.-G. Manea, and E. Manea. 2021. Estimation of ship repair time using the genetic algorithm. Polish Maritime Research 28 (3):88–99. doi:10.2478/pomr-2021-0036.
   Web of Science ®Google Scholar
• Zaytar, M. A., and C. E. Amrani. 2016. Sequence to sequence weather forecasting with long short-term memory recurrent neural networks. International Journal of Computer Applications 143 (11):7–11. doi:10.5120/ijca2016910497.
   Google Scholar