Estimation of wind speed by artificial intelligence method: A case study

Year 2024, Volume: 10 Issue: 5, 1347 - 1361, 10.09.2024

Enas F. Aziz Raid W. Daoud Sameer Algburi Omer Ahmed , Khalil F. Yassen

Abstract

Wind speed changes from one region to another due to several influencing variables. In this article, a software method has been proposed to determine the future wind speed at any time and under any conditions. Neural Networks were used with engineering data regarding the method of education, training algorithms, and different activation functions between the input and output layers, each according to the nature of the data that would be generated. Back-propagation Neural was used with three variables chosen to be the inputs for the learning and training network (wind speed, humidity, and time), which are considered the most important in determining the proposed or expected speed at the relevant time and place. The hidden layer consists of 10 neurons, which are determined according to the precision of output. After comparing the measurements from the weather system with the expected values, a very tiny percentage of error was found since these readings are regarded as acceptable and aid in the problem-solving process for running companies and researchers. The error rate recorded in this work ranged between (3 * 10-3 and 3 * 10-5), and the average number of attempts for the training and examination process reached 33 attempts, as it is known that neural networks carry out the training process based on specific mathematical functions and closed loops that depend on the lowest possible error rate.

Keywords

Artificial Intelligence, Assessment, Neural Network, Wind Energy

References

• [1] Ibrahim AM, Ibraheem RR, Weli RB. Energy saving in batteries using the photovoltaic system. Al-Kitab J Pure Sci 2023;4:78–94. [CrossRef]
• [2] Sunil Kumar K, Palanisamy R, Aravindh S, Mohan GD. Design and analysis of windmill blades for domestic applications. Int J Mech Engineer Technol 2017;8:25–36.
• [3] Saleh AN, Ahmed OK, Attar AA, Abdullah AA. Impact of nano-silica (SiO2) on thermic properties of concrete. J Therm Engineer 2024;10:746–755. [CrossRef]
• [4] BP. Statistical Review of World Energy 2022. Available at: https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2022-full-report.pdf. Accessed Aug 22, 2024.
• [5] Wiatros-Motyka M. EMBER Global Electricity Review 2023. Available at: https://ember-climate.org/insights/research/global-electricity-review-2023/. Accessed Aug 22, 2024.
• [6] Ibrahim AK, Ahmed OK. Evaluation of the performance of the photovoltaic Trombe wall in the Iraqi snoididnoc. NTU J Renew Energy 2023;5:47–60. [CrossRef]
• [7] Abbas EF, Aziz SA. The impact of air gap width on the free thermal load in the Trombe wall contains a phase change material. Al-Kitab J Pure Sci 2022;2:264–275. [CrossRef]
• [8] Abbas AK, Hussain AST. Efficient performance technical selection of positive buck-boost converter. Al-Kitab J Pure Sci 2022;2:20–38. [CrossRef]
• [9] Ahmed OK, Algburi S, Ali ZH, Ahmed AK, Shubat HN. Hybrid solar chimneys : A comprehensive review. Energy Rep 2022;8:438–460. [CrossRef]
• [10] Eltayeb WA, Somlal J, Kumar S, Rao SK. Design and analysis of a solar-wind hybrid renewable energy tree. Results Engineer 2023;17:100958. [CrossRef]
• [11] Bilgili M, Alphan H, Aktaş AE. Growth in turbine size and technological development of modern commercial large scale wind turbines in Türkiye. J Therm Engineer 2024;10:503–516. [CrossRef]
• [12] Abdullah AA, Attulla FS, Ahmed OK, Algburi S. Effect of cooling method on the performance of PV/Trombe wall: Experimental assessment. Therm Sci Engineer Prog 2021;30:101273. [CrossRef]
• [13] BP. Statistical Review of World Energy 2021. Available at https://www.bp.com/content/dam/bp/business-sites/en/global/corporate/pdfs/energy-economics/statistical-review/bp-stats-review-2021-full-report.pdf. Accessed Aug 22, 2024.
• [14] Gianfelice M, Aboshosha H, Ghazal T. Real-time wind predictions for safe drone flights in Toronto. Results Engineer 2022;15:100534. [CrossRef]
• [15] Kumar KS, Arun S, Mohan A, Muniamuthu S. Experimental analysis of noise and vibration reduction in windmill gear box for 5mw wind turbine. Int J Mech Engineer Technol 20216;7:76–85.
• [16] Hussein HI, Ghadhban AM. Hybrid Pv / wind / battery / diesel generator energy system for Hyderabad City, Pakistan 2015;8:124–138. [CrossRef]
• [17] Lee M, He G. An empirical analysis of applications of artificial intelligence algorithms in wind power technology innovation during 1980–2017. J Clean Prod 2021;297:126536. [CrossRef]
• [18] Kazem HA, Chaichan MT. Status and future prospects of renewable energy in Iraq. Renew Sustain Energy Rev 2012;16:6007–6012. [CrossRef]
• [19] Berkache A, Boumehani A, Noura B, Kerfah R. Numerical investigation of 3D unsteady flow around a rotor of vertical axis wind turbine darrieus type H. J Therm Engineer 2022;8:691–701.v
• [20] Masaaf Y, El Kadi YA, Baghli FZ. Levelized cost of energy and storage of compressed air energy storage with wind and solar plants in Morocco. J Therm Engineer 2024;10:847–856. [CrossRef]
• [21] Blanchard T, Samanta B. Wind speed forecasting using neural networks. Wind Engineer 2020;44:33–48. [CrossRef]
• [22] Yang Z, Wang J. A hybrid forecasting approach applied in wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm. Energy 20218;160:87–100. [CrossRef]
• [23] Cinar AC, Natarajan N. An artificial neural network optimized by grey wolf optimizer for prediction of hourly wind speed in Tamil Nadu, India. Intell Syst Appl 2021;16:200138. [CrossRef]
• [24] Zhao E, Sun S, Wang S. New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight. Data Sci Manage 2022;5:84–95. [CrossRef]
• [25] Wang H, Li Y, Xiong M, Chen H. A combined wind speed prediction model based on data processing, multi-objective optimization and machine learning. Energy Rep 2023;9:413–421. [CrossRef]
• [26] Nascimento EGS, de Melo TAC, Moreira DM. A transformer-based deep neural network with wavelet transform for forecasting wind speed and wind energy. Energy 2023;278:127678. [CrossRef]
• [27] Mahdi BH, Yousif KM, Melhum AI. Application of artificial neural network to predict wind speed: Case study in Duhok City, Iraq. J Phys Conf Ser 2021;1829:012002. [CrossRef]
• [28] Hussain ZS, Danha NY, Muheden KM, Kareem SW. Wind speed prediction for Duhok City applied recurrent neural network Int J Intell Syst Appl Engineer 2022;10:180–188.
• [29] Zhang L, He S, Cheng J, Yuan Z, Yan X. Research on neural network wind speed prediction model based on improved sparrow algorithm optimization. Energy Rep 2022;8:739–747. [CrossRef]
• [30] Bhattacharya M, Sinha M. Basin scale wind-wave prediction using empirical orthogonal function analysis and neural network models. Results Geophys Sci 2021;8:100032. [CrossRef]
• [31] Sunil Kumar K, Muniamuthu S, Tharanisrisakthi BT. An investigation to estimate the maximum yielding capability of power for mini venturi wind turbine. Ecol Engineer Environ Technol 2022;23:72–78. [CrossRef]
• [32] Ofori-Ntow E Jnr, Ziggah YY, Rodrigues MJ, Relvas S. A hybrid chaotic-based discrete wavelet transform and Aquila optimisation tuned-artificial neural network approach for wind speed prediction. Results Engineer 2022;14:100399. [CrossRef]
• [33] Syama S, Ramprabhakar J, Anand R, Guerrero JM. A hybrid extreme learning machine model with Lévy flight Chaotic Whale Optimization algorithm for wind speed forecasting. Results Engineer 2023;19:101274. [CrossRef]
• [34] Darwish ASK, Sayigh AAM. Wind energy potential in Iraq. Sol Wind Technol 1988;5:215–222. [CrossRef]
• [35] Ahmed OK. Assessment of wind speed for electricity generation in Makhool Mountain in Iraq. Int J Inven Engineer Sci 2014;2:5–10.
• [36] Mahmood FH, Resen AK, Khamees AB. Wind characteristic analysis based on Weibull distribution of Al-Salman site, Iraq. Energy Rep 2020;6:79–87. [CrossRef]
• [37] Darwish AS, Shaaban S, Marsillac E, Mahmood NM. A methodology for improving wind energy production in low wind speed regions, with a case study application in Iraq. Comp Ind Engineer 2019;127:89–102. [CrossRef]
• [38] Al-Alawy IT, Mohammed AIY. A mathematical model of wind energy for selected locations in Iraq. Sol Wind Technol 1984;1:187–191. [CrossRef]
• [39] Al-Azzawi SI, Zeki NA. Comparison between the characteristics of wind power calculations and solar radiation energy of some meteorological stations in lraq. Solar Wind Technology 1987;4:513516. [CrossRef]
• [40] AL-Azzawi S I, ZekiN.Comparison between the characteristics of wind power calculations and solar radiation energy of some meteorological stations in lraq. Solar & Wind Technology 1987; ;513-516, Al-Rijabo WI, Fayik LM, Jaro BB. Wind speed distribution in Ninava Governorate. J Educ Sci 2009;22:56–74. [CrossRef]
• [41] Ibrahim AR, Saeed MA. Wind energy potential in Garmyan. Diyala J Pure Sci 2010;6:170–182.
• [42] Mishaal AK, Abd Ali AM, Khamees AB. Wind distribution map of Iraq - A comparative study. IOP Conf Ser Mater Sci Engineer 2020;928:022044. [CrossRef]
• [43] Kurugundla SK, Muniamuthu S, Raja P, Mohan KR. Measurement of temperature flow analysis by condition monitoring system for WTG gear box to evaluate the thermal performance associated with plant load factor. J Therm Engineer 2023;9:979–987. [CrossRef]
• [44] Duffie JA, Beckman WA. Solar Engineering of Thermal Processes. Fourth ed. New Jersey: John Wiley & Sons; 2013. [CrossRef]
• [45] Amahjour N, Khamlichi A. Analysis of the effect of an obstacle on the wind energy potential. MATEC Web Conf 2016;83:1–3. [CrossRef]
• [46] Ackermann T, ed. Wind Power in Power Systems. New Jersey: John Wiley & Sons; 2005.
• [47] Patel MR. Wind and Solar Power Systems: Design, Analysis, and Operation. Second edition. Boca Raton: CRC Press; 2005. pp. 1–448. [CrossRef]
• [48] Al-Jibouri DOKA. Feasibility of using wind energy for irrigation in Iraq. Int J Mech Engineer Technol 2014;5:62–72.