Estimation of the electricity to be generated at different wind speeds and turbines through fuzzy logic and ANN, A case study of Balıkesir

Year 2024, Volume: 9 Issue: 1, 115 - 133, 22.03.2024

Zuleyha Ok Davarcı , Onur Akar

https://doi.org/10.58559/ijes.1412279

Cited By: 1

Abstract

One of the most significant factors determining the development level of the world’s countries in the economic domain is energy. As technology makes progress, the need of countries for energy continuously increases in parallel with that. Meeting such increasing energy demand with fossil fuels for many years has damaged the living standards of all living beings. Both of these two circumstances have caused an increase in demand for Renewable Energy Resources (RER), with wind power being one of them. In the present study, monthly wind speed, temperature, and pressure measurement data obtained from the Wind Power Plant (WPP) located in the Gonen District of Balikesir Province were averaged out. Using this data and the output data of electricity amounts from different turbine types, an electric power production estimation model was formed through the Artificial Neural Network (ANN) and Fuzzy Logic (FL) methods. It was intended to determine the electric power required to be generated by the model formed through ANN and FL. When the estimations obtained by the ANN and FL were compared, it was observed that the results were correct and coherent.

Keywords

RER, WPP, Artificial neural network, Fuzzy logic

References

• [1] Ilkilic C. Wind energy and assessment of wind energy potential in Turkey. Renewable and Sustainable Energy Reviews 2012; 16(1): 1165–1173.
• [2] Wai RJ, Wang WH, Lin CY. High - Performance Stand - Alone Photovoltaic Generation System. IEEE Transactions on Industrial Electronics 2008; 55(1): 240-250.
• [3] Zhang S, Zhu C, Sin JKO, Mok PKT. A novel ultrathin elevated channel low-temperature poly-Si.TFT. IEEE Electron Device Lett 1999; 20: 569–571.
• [4] Memis S, Karakoc O. An Application of Soft Decision-Making Methods to Energy Planning of Turkey. In Proceedings of the 2nd Rumeli Energy and Design Symposium for Sustainable Environment, İstanbul, Türkiye, 17–18 February 2022; 146–157.
• [5] Horosan MB, Kilic HS. A multi-objective decision-making model for renewable energy planning: The case of Turkey. Renewable Energy 2022; 193: 484-504.
• [6] Aksoy A. Integrated model for renewable energy planning in Turkey. International journal of green energy 2019; 16(1): 34-48.
• [7] Wegmuller M, von der Weid JP, Oberson P, Gisin N. High resolution fiber distributed measurements with coherent OFDR. in Proc. ECOC’00 2000; 11(4): 109.
• [8] Azad K, Rasul MG, Islam R, Shishir IR. Analysis of wind energy prospect for power generation by three weibull distribution methods. Energy procedia 2015; 75: 722-727.
• [9] Monfared M, Rastegar H, Kojabadi HM. A new strategy for wind speed forecasting using artificial intelligent methods. Renewable Energy 2009; 34(3): 845–848.
• [10] Kose R, Ozgur M, Arif EO, Tugcu A. The analysis of wind data and wind energy potential in Kutahya, Turkey. Renewable and Sustainable Energy Reviews 2004; 8: 277–288.
• [11] Aslan M. Archimedes optimization algorithm based approaches for solving energy demand estimation problem: a case study of Turkey. Neural Computing and Applications 2023; 35(26): 19627-19649.
• [12] Mayilsamy G, Palanimuthu K, Venkatesweren R. et. al. A Review of State Estimation Techniques for Grid- Connected PMSG-Based Wind Turbine Systems. Energies 2023; 16(2): 634.
• [13] Jiang Z. Installation of offshore wind turbines: A technical review. Renewable and Sustainable Energy Reviews 2021; 139: 110576.
• [14] Da Rosa AV, Ordonez JC. Fundamentals of renewable energy processes. Academic Press 2021.
• [15] Erdogdu E. On the wind energy in Turkey. Renewable and Sustainable Energy Reviews 2009; 13(6-7): 1361- 1371.
• [16] Togrul IT, Ertekin CA. Statistical Investigation on the Wind Energy Potential of Turkey’s Geographical Regions Energy Source. Part A: Recovery, Utilization, and Environmental Effects 2011; 33(15): 399- 1421.
• [17] Bilgili M, Sahin B, Kahraman A. Wind energy potential in Antakya and İskenderun regions, Turkey. RenewableEnergy 2004; 29: 1733–1745.
• [18] IRENA (International Renewable Energy Agency) Wind Energy, 2022.
• [19] Negnevitsky M, Potter C. Innovative short-term wind generation prediction techniques. Proceedings of the IEEE/PES general meeting, Montreal, Canada, June 18–22. 2006.
• [20] Pousinho HMI, Mendes VMF, Catalao JPDS. A hybrid PSO–ANFIS approach for short-term wind power prediction in Portugal. Energy Convers Manage 2011; 52: 397–402.
• [21] Akpinar EK, Akpinar S. An assessment on seasonal analysis of wind energy characteristics and wind turbine characteristics. Energy Conversion and Management 2005; 46(11-12): 1848-1867.
• [22] Sareen K, Panigrahi BK, Shikhola T, Sharma R. An imputation and decomposition algorithms based integrated approach with bidirectional LSTM neural network for wind speed prediction. Energy 2023; 278: 127799.
• [23] Jin LV. Summary of Artificial Neuron Model Research. Industrial Electronics Society. 33 rd Annual Conference of the IEEE, 5-8 Nov. 2007.
• [24] Fausett LV. Fundamentals of neural networks: architectures, algorithms and applications. Pearson Education India, 1993.
• [25] Sozen A, Arcaklıoglu E, Ozkaymak M. Turkey’s net energy consumption. Applied Energy 2005; 81(2): 209-221.
• [26] Sozen A, Arcaklıoglu E. Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey. Energy Policy 2007; 35(10): 4981-4992.
• [27] Kavaklıoglu K, Ceylan H, Ozturk HK, Canyurt OE. Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks. Energy Conversion and Management 2009; 50(11): 2719-2727.
• [28] Geem ZW, Roper WE. Energy demand estimation of South Korea using artificial neural network. EnergyPolicy 2009; 37(10): 4049-4054.
• [29] Ekonomou L. Greek long-term energy consumption prediction using artificial neural networks. Energy 2010; 35: 512-517.
• [30] Limanond T, Jomnonkwao S, Srikaew A. Projection of future transport energy demand of Thailand. Energy Policy 2011; 39(5): 2754-2763.
• [31] Geem ZW. Transport energy demand modeling of South Korea using artificial neural network. Energy Policy 2011; 39(8): 4644-4650.
• [32] Akar O. Estimation through ANN of Voltage Drop Resulting from Overloads on Power Transformers. European Journal of Technique 2022; 12(2): 198-203.
• [33] Kitajima T, Yasuno T. Output prediction of wind power generation system using complex-valued neural network. SICE Annual Conference 2010.
• [34] Zadeh LA. Fuzzy Sets. Information and control 1965; 8(3): 338-353.
• [35] Zhu B, Chen MY, Wade N, Ran L. A prediction model for wind farm power generation bas-ed on fuzzy modeling. Procedia Environmental Sciences 2012; 12: 122-129.
• [36] Landberg L. A mathematical look at a physical power prediction model. Wind Energy: An International Journal for Progress and Applications in Wind Power Conversion Technology 1998; 1(1): 23-28.
• [37] Landberg L. Short-term prediction of the power production from wind farms. Journal of Wind Engineering and Industrial Aerodynamics 1999; 80(1-2): 207-220.
• [38] Kelesoglu O, Firat A. Determination of Heat Loss in Brick Wall and Installation with Artificial Neural Networks. Firat University Science and Engineering 2006; 18(2): 133-141.
• [39] Özdem B. Determination and Evaluation of Electric Power Generation Strategy with Fuzzy Logic Method. Master's Thesis, Karabük University, 2022.
• [40] Ahmad I. et. al. Fuzzy logic control of an artificial neural network-based floating offshore wind turbine model integrated with four oscillating water columns. Ocean Engineering 2023; 269: 113578.