FORECASTING WEIBULL PARAMETERS WITH A NOVEL ALTERNATIVE GRAPHICAL TECHNIQUE FOR LOW WIND SPEEDS
DOI:
https://doi.org/10.11113/jurnalteknologi.v84.17987Keywords:
Wind speed, Weibull, histogram, alternative graphical method, goodness of fitAbstract
This paper analyses wind speed estimation for Weibull distribution using various methods. According to a previous study, the existing methods primarily target areas with moderate to high-velocity rates, and Malaysia is a tropical country with pleasant breezes all year. As a result, this research aims to devise the most efficient method of forecasting wind speeds in low-speed areas. The researcher compared existing methods such as the Moment of Method, Empirical Method, Graphical Method, Maximum Likelihood Method and the newly proposed Alternative Graphical method. The finding indicates that the novel proposed approach, the Alternative Graphical Method, is superior regarding Goodness of Fit, Kolmogorov Smirnov and Chi-Square. For Kolmogorov Smirnov, the Alternative Graphical Method is 3.4 % better than the second-best method. At the same time, the usage of Chi-Square is again at a top position, with a 61 % disparity between it and the second and third best places. However, the Alternative Graphical Method is in second place for Anderson Darling, but the forecast performance with a minimum difference of 0.3 %. These findings imply that the Alternative Graphical Method capable of making more precise predictions than current methods.
References
K. Bagci, T. Arslan, and H. E. Celik. 2021. Inverted Kumarswamy Distribution for Modeling the Wind Speed Data: Lake Van, Turkey. Renew. Sustain. Energy Rev. 135(1): 110110.
J. Jayapriya, D. Muruganandam, D. Raguraman, B. Senthilkumar, and V. Dhinakaran. 2019. Design and Fabrication of Multi-rotor Horizontal Axis Wind Turbine. Int. J. Eng. Adv. Technol. 8(6): 3500-3504.
A. Aarib, A. El Moudden, A. El Moudden, and A. Hmidat. 2021. Control and Investigation of Operational Characteristics of Variable Speed Wind Turbines with Doubly Fed Induction Generators. Walailak J. Sci. Technol. 18(4): 1-13.
N. Tenzin and R. P. Saini. 2019. Wind and Solar Resource Potential Assessment in Bhutan. Int. J. Eng. Adv. Technol. 8(3): 391-395.
S. K. Korkua. 2015. Fault-tolerant Control Using Adaptive Time-frequency Method in Bearing Fault Detection for DFIG Wind Energy System. Walailak J. Sci. Technol. 12(2): 111-124.
E. Dokur and M. Kurban. 2015." Wind Speed Potential Analysis Based on Weibull Distribution. Balk. J. Elec. Comp. Eng. 3(4): 231-235.
S. A. Akdaǧ and A. Dinler. 2009. A New Method to Estimate Weibull Parameters for Wind Energy Applications. Energy Convers. Manag. 50(7): 1761-1766.
S. A. Ahmed. 2013. Comparative Study of Four Methods for Estimating Weibull Parameters for Halabja, Iraq. Int. J. Phys. Sci. 8(5): 186-192.
M. Celeska, K. Najdenkoski, V. Stoilkov, A. Buchkovska, Z. Kokolanski, and V. Dimchev. 2015. Estimation of Weibull Parameters from Wind Measurement Data by Comparison of Statistical Methods. International Conference on Computer as a Tool (EUROCON 2015). 1: 1-6.
K. Sopian, M. Y. Othman, and A. Wirsat. 1995. The Wind Energy Potential of Malaysia. Renew. Energy. 6(8): 1005-1016.
A. Zaharim, S. K. Najid, A. M. Razali, and K. Sopian. 2008. The Suitability of Statistical Distribution in Fitting Wind Speed Data. WSEAS Trans. Math. 7(1): 386-389.
A. Belhamadia, M. Mansor, and M. A. Younis. 2014. A Study on Wind and solar Energy Potentials in Malaysia. Int. J. Renew. Energy Res. 4(4): 1042-1048.
A. Albani, M. Ibrahim, and K. Yong. 2013. Wind Energy Investigation in Northern Part of Kudat, Malaysia. EAAS J. 2(2): 14-22.
M. N. Kamarudin, A. R. Husain, M. N. Ahmad, and Z. Mohamed. 2015. Model and Analysis of Wind Speed Profile using Artificial Neural Network - Feasibility Study in Peninsular Malaysia. J. Teknol. 74(1): 77-81.
B. Safari and J. Gasore. 2010. A Statistical Investigation of Wind Characteristics and Wind Energy Potential based on the Weibull and Rayleigh Models in Rwanda. Renew. Energy. 35(12): 2874-2880.
F. M. Noman, G. A. Alkawsi, D. Abbas, A. A. Alkahtani, S. K. Tiong, and J. Ekanayake. 2020. Comprehensive Review of Wind Energy in Malaysia: Past, Present, and Future Research Trends. IEEE Access. 8(1): 124526-124543.
P. Wais. 2017. A Review of Weibull Functions in Wind Sector. Renew. Sustain. Energy Rev. 70(1): 1099-1107.
M. Adnan, J. Ahmad, S. F. Ali, and M. Imran. 2021. A Techno-economic Analysis for Power Generation through Wind Energy: A Case Study of Pakistan. Energy Rep. 7(1): 1424-1443.
I. Pobočíková, Z. Sedliačková, J. Šimon, and D. Jurášová. 2020. Statistical Analysis of the Wind Speed at Mountain Site Chopok, Slovakia, using Weibull Distribution. IOP Conference Series: Materials Science and Engineering. 776(1): 012114.
S. Brusca. 2014. A New Statistical based Energetic-economic Methodology for Wind Turbine Systems Evaluation. Energy Procedia. 45(1): 180-187.
P. K. Chaurasiya, S. Ahmed, and V. Warudkar. 2018. Comparative Analysis of Weibull Parameters for Wind Data Measured from Met-mast and remote Sensing Techniques. Renew. Energy. 115(1): 1153-1165.
J. Wang 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. Appl. Energy. 230(1): 429-443.
A. K. Azad, M. G. Rasul, M. M. Alam, S. M. A. Uddin, and S. K. Mondal. 2014. Analysis of Wind Energy Conversion System Using Weibull Distribution. Procedia Eng. 90(1): 725-732.
C. Yu, Y. Li, Y. Bao, H. Tang, and G. Zhai. 2018. A Novel Framework for Wind Speed Prediction based on Recurrent Neural Networks and Support Vector Machine. Energy Convers. Manag. 178(24): 137-145.
K. Al-Salem and W. Al-Nassar. 2018. Assessment of Wind Energy Potential at Kuwaiti Islands by Statistical Analysis of Wind Speed Data. 3rd International Conference on Advances on Clean Energy Research. 51(1): 1-9.
S. L. B. S. M. Lawana, W. A. W. Z. Abidin, A. M. Lawan, M. Mustapha. 2015. ANN and GIS-assisted Methodology for Wind Resource Assessment (WRA) in Sarawak. J. Teknol. 77(12): 75-80.
A. Singlitico, J. Østergaard, and S. Chatzivasileiadis. 2021. Onshore, Offshore or In-turbine Electrolysis? Techno-economic Overview of Alternative Integration Designs for Green Hydrogen Production into Offshore Wind Power Hubs. Renew. Sust Energ. Transit. 1(1): 100005.
V. Sohoni, S. Gupta, and R. Nema. 2016. A Comparative Analysis of Wind Speed Probability Distributions for Wind Power Assessment of Four Sites. Turk J Elec Eng Comp Sci. 24(6): 4724-4735.
Z. Saberi, A. Fudholi, and K. Sopian. 2019. Potential Evaluation of Wind Energy in Kuala Terengganu, Malaysia through Weibull Distribution Method. IOP Conference Series: Earth and Environmental Science. 268(1): 012074.
D. Indhumathy, C. V Seshaiah, and K. Sukkiramathi. 2014. Estimation of Weibull Parameters for Wind speed calculation at Kanyakumari in India. Int. J. Innov. Res. Sci. Eng. Technol. 3(1): 8340-8345.
A. A. Teyabeen, F. R. Akkari, and A. E. Jwaid. 2017. Comparison of Seven Numerical Methods for Estimating Weibull Parameters for Wind Energy Applications. 2017 UKSim-AMSS 19th International Conference on Modelling and Simulation. 2: 173-178.
A. Albani, M. Z. Ibrahim, C. M. I. C. Taib, and A. A. Azlina, 2017. The Optimal Generation Cost-based Tariff Rates for Onshore Wind Energy in Malaysia. Energies. 10(8): 1114.
C. Carrillo, J. Cidrás, E. Díaz-Dorado, and A. F. Obando-Montaño. 2014. An Approach to Determine the Weibull Parameters for Wind Energy Analysis: The Case of Galicia (Spain). Energies. 7(4): 2676-2700.
K. Azad, M. Rasul, P. Halder, and J. Sutariya. 2019. Assessment of Wind Energy Prospect by Weibull Distribution for Prospective Wind Sites in Australia. Energy Procedia. 160(1): 348-355.
A. Qawasmi and S. Kiwan. 2017. Effect Weibull Distribution Parameters Calculating Methods on Energy Output Effect Weibull Distribution Parameters Calculating Methods on Energy Output of a Wind Turbine : A Study Case. Int. J. Energy Environ. Eng. 14(2): 163-173.
Z. Saberi, A. Fudholi, and K. Sopian. 2020. Fitting of Weibull Distribution Method to Analysis Wind Energy Potential at Kuala Terengganu, Malaysia. J. Adv. Res. Fluid Mech. Therm. Sci. 66(1): 1-11.
P. K. Chaurasiya, S. Ahmed, and V. Warudkar. 2017. Study of Different Parameters Estimation Methods of Weibull Distribution to Determine Wind Power Density using Ground based Doppler SODAR instrument. Alex. Eng. J. 2299-2311.
A. Siddiqui et al. 2015. Determination of Weibull Parameter by Four Numerical Methods and Prediction of Wind Speed in Jiwani (Balochistan). J. Basic Appl. Sci. 11(1): 62-68.
D. K. Kidmo,R. Danwe, S. Y. Doka, and N. Djongyang. 2014. Performance Analysis of Methods for Estimating Weibull. Int. J. Fundam. Appl. Sci. 6(2): 155-176.
K. S. R. Murthy and O. P. Rahi. 2014. Estimation of Weibull Parameters Using Graphical Method for Wind Energy Applications. 8th National Power Systems Conference. 1-6.
M. Alrashidi, M. Pipattanasomporn, and S. Rahman. 2020. Metaheuristic Optimisation Algorithms to Estimate Statistical Distribution Parameters for Characterising Wind Speeds. Renew. Energy.149(7): 664-681.
F. G. Akgül, B. Şenoʇlu, and T. Arslan. 2016. An Alternative Distribution to Weibull for Modelling the Wind Speed Data: Inverse Weibull Distribution. Energy Convers. Manag. 114(7): 234-240.
P. A. Costa Rocha, R. C. de Sousa, C. F. de Andrade, and M. E. V. da Silva. 2012. Comparison of Seven Numerical Methods for Determining Weibull Parameters for wind Energy Generation in the Northeast Region of Brazil. Appl. Energy. 89(1): 395-400.
W. Werapun, Y. Tirawanichakul, and J. Waewsak. 2015. Comparative Study of Five Methods to Estimate Weibull Parameters for Wind Speed on Phangan Island, Thailand. Energy Procedia. 79: 976-981.
S. Marih, L. Ghomri, and B. Bekkouche. 2020. Evaluation of the Wind Potential and Optimal Design of a Wind Farm in the Arzew Industrial Zone in Western Algeria. Int J. Renew. En. Dev. 9(2): 177-187.
A. B. Asghar and X. Liu. 2018. Estimation of Wind Speed Probability Distribution and Wind Energy Potential Using Adaptive Neuro-fuzzy Methodology. Neurocomputing. 287(1): 58-67.
A. K. Azad, M. G. Rasul, and T. Yusaf. 2014. Statistical Diagnosis of the Best Weibull Methods for Wind Power Assessment for Agricultural Applications. Energies. 7(5): 3056-3085.
S. Nortazi. 2019. Analisis Potensi Tenaga Angin di Malaysia: Suatu Kajian Terhadap Taburan Kebarangkalian Angin. Universiti Kebangsaan Malaysia.,
D. Derome, H. Razali, A. Fazlizan, A. Jedi, and K. Purvis-roberts. 2022. Determination of Optimal Time-average Wind Speed Data in the Southern Part of Malaysia. Baghdad Sci. J. 19(5): 1111-1122.
N. Sanusi, A. Zaharim, S. Mat, and K. Sopian. 2017. A Weibull and Finite Mixture of the Von Mises Distribution for Wind Analysis in Mersing, Malaysia. Int. J. Green Energy. 14(12): 1057-1062.
I. Susanto. 2018. Kelebihan dan Kekurangan Open Source R. Prosiding Seminar Nasional Pengembangan Pendidikan, 2011. [Online]. Available: http://irwansusanto.staff.mipa.uns.ac.id/2011/08/21/kelebihan-dan-kekurangan-open-source-r/. [Accessed: 19-Oct-2018].
Y. Huang, S. Liu, and L. Yang. 2018. Wind Speed Forecasting Method Using EEMD and the Combination Forecasting Method based on GPR and LSTM. Sustainability. 10(0): 3693.
A. M. Razali, M. S. Sapuan, K. Ibrahim, A. Zaharim, and K. Sopian. 2009. Mapping of Annual Extreme Wind Speed Analysis from 12 Stations in Peninsular Malaysia. 8th WSEAS International Conference on System Science and Simulation in Engineering. 397-403.
Abdul Muhaimin Mahmud. 2016. A Post-installation Analysis of Solar PV-diesel Hybrid Systems for School Electrification in Sabah, Malaysia. Loughborough University.
N. Sanusi and A. Zaharim. 2015. An Initial Study on Wind Speed in Mersing, Malaysia. Computer Applications in Environmental Sciences and Renewable Energy. 37(1): 110-113.
K. S. R. Murthy, O. P. Rahi, P. Sonkar, and S. Ram. 2017. Longterm Analysis of Wind Speed and Wind Power Resource Assessment for the Site Vijayawada, Andhra Pradesh, India. 2017 6th International Conference on Computer Applications in Electrical Engineering - Recent Advances, CERA 2017. 140-145.
S. N. Ashwindran, A. A. Azizuddin, A. N. Oumer, and M. Z. Sulaiman. 2021. A Review on the Prospect of Wind Power as an Alternative Source of Energy in Malaysia. IOP Conference Series: Materials Science and Engineering. 1078(1): 1-16.
Downloads
Published
Issue
Section
License
Copyright of articles that appear in Jurnal Teknologi belongs exclusively to Penerbit Universiti Teknologi Malaysia (Penerbit UTM Press). This copyright covers the rights to reproduce the article, including reprints, electronic reproductions, or any other reproductions of similar nature.
