Open Access Open Access  Restricted Access Subscription or Fee Access

Comparative Analysis of Wind Speed and Energy Potential Assessment of Two Distribution Models in Medan, Indonesia

(*) Corresponding author

Authors' affiliations



Wind is an environmentally friendly form of energy and currently many researchers focus on the development of this energy, because this energy can be used as a promising future Renewable Energy Source (RES). The development of equipment for the use of this energy is an expected and necessary contribution, one of which is as a tool to analyse its energy potential against the probability density function (Pdf). These two models (Weibull and Rayleigh distributions) are proposed for the analysis and the evaluation of wind speed potential in Medan, Indonesia. Data for analysis and evaluation have been taken for three years (2017-2019) obtained from the Meteorology, Climatology, and Geophysics Agency (MCGA), owned by the Government of Indonesia. The purpose of this study is to get the best model proposed to be applied. Statistical analysis and evaluation with three coefficients, namely the correlation model (R2), Chi-Square (χ2) and Root Mean Square Error (RMSE) has been chosen as a model that can be applied. In addition to analysis and evaluation, the form factor (k) and the scale (c) are also determined under different conditions and situations. These two parameters are very important to determine the potential of wind energy in Medan, Indonesia. It has been found out that the magnitude of R2 for Weibull distribution is between 0.934-0.978 in May and June, while for Rayleigh distribution it is between 0.195-0.788. The wind speed for three years (2017-2019) had minimum, maximum, and average speeds between 2.33-2.71 m/s, 7.35-7.71 m/s, and 4.91-5.06 m/s, respectively. On the other hand, the annual electrical power is about 72.21-79.46 W/m2, while the energy is about 623.88-686.55 J. The annual k and c values vary, with k ranging from 4,560-5,266 and c ranging from 5,347-5,496, respectively, with an average k of 4,914 and c of 5,405.
Copyright © 2023 Praise Worthy Prize - All rights reserved.


Wind Speed; Distribution Model Comparison; Probability Density Function (Pdf)

Full Text:



Ram Avtar, Netrananda Sahu, Ashwani Kumar Aggarwal, Shamik Chakraborty, Ali Kharrazi, Ali P Yunus, Jie Dou, Tonni Agustiono Kurniawan., Exploring Renewable Energy Resources Using Remote Sensing and GIS-A Review, Resour., vol. 8, pp. 1-23, 2019.

Arvind Chel and Geetanjali Ksushik, Renewable energy technologies for sustainable development of energy efficient building, Alexandria Eng. J., vol. 57, pp. 655-669, 2018.

Pappas D, Energy and Industrial Growth in India: The Next Emissions Superpower, Energy Procedia, vol. 105, pp. 3656-3662, 2017.

Schmid G, The development of renewable energy power in India: which policies have been effective? Energy Policy, Energy Policy, vol. 45, pp. 317-326, 2012.

Bengt Johansson, Security aspects of future renewable energy systems-A short overview, Energy, vol. 61, no. 1, pp. 598-605, 2013.

Dolf Gielena, Francisco Boshell, Deger Saygin, Morgan D.Bazilian, Nicholas Wagner, Ricardo Gorini., The role of renewable energy in the global energy transformation, Energy Strateg. Rev., vol. 24, pp. 38-50, 2019.

Elena P. Wadim Strielkowski, Lubomír Civín, Elena Tarkhanova, Manuela Tvaronavičienė, Renewable Energy in the Sustainable Development of Electrical Power Sector: A Review, Energies, vol. 14, no. 24, 2021.

Asumadu-Sarkodie S and Owusu P A, A review of Ghana's energy sector national energy statistics and policy framework, Cogent Eng., vol. 3, 2016.

B. Skogen, K; Helland, H; Kaltenborn, Concern about climate chang, biodiversity loss, habitat degradation and landscape change: Embedded in diffrent packages of environmental concern, J. Nat. Conserv, vol. 44, pp. 12-20, 2018.

G. Goncalves, H.M; Lourenco, T.F; Siva, Green buying behavior and the theory of consumption values: A fuzzy-set approach, J. Bus Res., vol. 69, pp. 1494-1491, 2016.

V. Urban, J; Bahnik, D; Demczuk, R; Souto Maior, C; Vilasanti, Green comsumption does not make people cheat: Three attempts to replicate moral licensing effect due to pro-enviromental behavior, J. Environ. Psychol., vol. 63, pp. 139-147, 2019.

A. G. Vadim Manusov, Pavel Matrenin, Muso Nazarov, Svetlana Beryozkina, Murodbek Safaraliev, Inga Zicmane, Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems, Sustainability, vol. 15, no. 2, pp. 1-12, 2023.

Aoife M Foley, Paul G Leahy, Antonino Marvuglia, Eamon J McKeogh., Current methods and advances in forecasting of wind power generation, Renew. Energy Convers. Syst., vol. 37, no. 1, pp. 1-8, 2012.

Changtian Ying, Weiqing Wang, Jiong Yu, Qi Li, Donghua Yu, Jianhua Liu., Deep learning for renewable energy forecasting: A taxonomy, and systematic literature review, J. Clean. Prod., vol. 384, 2023.

Jerry L Holechek, Hatim M E Geli, Mohammed N Sawalhah, Raul Valde., A Global Assessment: Can Renewable Energy Replace Fossil Fuels by 2050?, Sustainability, vol. 14, no. 8, pp. 1-22, 2022.

Olabi AG, Obaideen K, Abdelkareem MA, AlMallahi MN, Shehata N, Alami AH, Mdallal A, Hassan AAM, Sayed ET. Wind Energy Contribution to the Sustainable Development Goals: Case Study on London Array. Sustainability. 2023; 15(5):4641.

Atte Harjanne and Janne M Korhonen, Abandoning the concept of renewable energy, Energy Policy, vol. 127, pp. 330-340, 2019.

D. Nallaperuma et al., Online Incremental Machine Learning Platform for Big Data-Driven Smart Traffic Management, IEEE Trans. Intell. Transp. Syst., vol. 20, no. 12, pp. 4679-4690, 2019.

H. Sheng, G; Xie, F; Gong, S; Pan, The role of cultural values in green purchasing intention: Empirical evidence from Chinese consumers, Int. J. Comsum. Stud., vol. 43, pp. 315-326, 2019.

Cihangir Kan, Yilser Devrim, Serkan Erylmaz., On the theoretical distribution of the wind farm power when there is a correlation between wind speed and wind turbine availability, Reliab. Eng. Syst. Saf., vol. 203, 2020.

Davi Ribeiro Lins, Kevin Santos Guedes, Anselmo Ramalho Pitombeira-Neto, Paulo Alexandre Costa Rocha, Carla Freitas de Andrade., Comparison of the performance of different wind speed distribution models applied to onshore and offshore wind speed data in the Northeast Brazil, Energy, vol. 278, no. Part A, 2023.

Mahesh K, Dr M V Vijayakumar, Gangadharaiah Y H., A Statistical Analysis and Datamining Approach for Wind Speed Predication, Int. J. Comput. Technol., vol. 14, no. 2, pp. 5465-5478, 2014.

Kumar S, CO2 emission reduction potential assessment using renewable energy in India, Energy, vol. 97, pp. 273-282, 2016.

Inaki Arto, Inigo Capellan-Perez, Rosa Lago, Gorka Bueno, Roberto Bermejo, The energy requirements of a developed world, Energy Sustain. Dev., vol. 33, pp. 1-13, 2016.

Alexandru Serban, Lizicz Simona Paraschiv, Spiru Paraschiv., Assessment of wind energy potential based on Weibull and Rayleigh distribution models, Energy Reports, vol. 6, pp. 250-267, 2020.

Wenxin Wang, Chaofan Qin, Jiuyu Zhang, Caifeng Wen, Guoqiang Xu., Correlation analysis of three-parameter Weibull distribution parameters with wind energy characteristics in a semi-urban environment, Energy Reports, vol. 8, pp. 8480-8498, 2022.

Ghalia Twfeek Basheer and Zakariya Yahya Algamal, Reliability Estimation of Three Parameters Weibull Distribution based on Particle Swarm Optimization, Pakistan J. Stat. Oper. Res., vol. 17, no. 1, pp. 35-42, 2021.

Huanyu Shi, Zhibao Dong, Nan Xiao, Qinni Huang., Wind Speed Distributions Used in Wind Energy Assessment: A Review, Front. Energy Res., pp. 1-14, 2021.

Shigang Qin and Deshun Liu, Distribution Characteristics of Wind Speed Relative Volatility and Its Influence on Output Power, Mar. Sci. Eng., vol. 11, pp. 1-17, 2023.

Hicham Bidaoui. Ikram El Abbasi. Abdelmajid El Bouardi. Abdelmoumen Darcherif., Wind Speed Data Analysis Using Weibull and Rayleigh Distribution Functions, Case Study: Five Cities Northern Morocco, Procedia Manuf., vol. 32, pp. 786-793, 2019.

Suwarno. Leong Jenn Hwai. Muhammad Fitra Zambak. and Indra Nisja. and Rohana., Assessment of Wind Energy Potential using Weibull Distribution Function as Wind Power Plant in Medan, North Sumatra, Int. J. Simulation--Systems, Sci. Technol., vol. 17, no. 41, 2016.

Suwarno and Rohana, Comparison model hargreaves, annandale and new model for estimation of solar radiation in Perlis, Malaysia, Indones. J. Electr. Eng. Comput. Sci., vol. 6, no. 2, pp. 286-293, May 2017.

Suwarno and M Fitra Zambak, The Probability Density Function for Wind Speed Using Modified Weibull Distribution, Int. J. Energy Econ. Policy, vol. 11, no. 6, pp. 44-550., 2021.

Husain R. Alsamamra, Saeed Salah, Jawad A.H. Shoqeir c, Ali J. Manasra, A comparative study of five numerical methods for the estimation of Weibull parameters for wind energy evaluation at Eastern Jerusalem, Palestine, Energy Reports, vol. 8, pp. 4801-4810, 2022.

Hao Chen, Stian Normann Anfinsen, Yngve Birkelund, Fuqing Yuan, Probability distributions for wind speed volatility characteristics: A case study of Northern Norway, Energy Reports, Volume 7, Supplement 5, 2021, Pages 248-255.

Ivana Pobocikova. Zuzana Sedliackova. Maria Michalkova., Application of Four Probability Distributions for Wind Speed Modeling, Procedia Eng., vol. 192, pp. 713-718, 2017.

Domenico Mazzeo, Giuseppe Oliveti, Ester Labonia., Estimation of wind speed probability density function using a mixture of two truncated normal distributions, Renew. energy, vol. 115, pp. 1260-1280, 2018.

Suwarno, Adi S, Ismail Daud, The Potential Wind To Electrical Energy Using A Linear Regression Method in Medan City, Pros. Semin. Nas. 2012, no. July, pp. 183-188, 2012.

Sandipan Sikdar, Paras Tehria, Matteo Marsili, Niloy Ganguly, Animesh Mukherjee ., On the effectiveness of the scientific peer-review system: a case studyof the Journal of High Energy Physics, Int. J. Digit. Libr., vol. 21, pp. 93-107, 2020.

E. K. O. Oghenekevwe Oghoghorie, Patrick Okechukwu Ebunilo, Development of a Savonius Vertical Axis Wind Turbine Operated Water Pump, J. Appl. Res. Ind. Eng., vol. 7, no. 2, pp. 190-202, 2020.

H. I. H. Ali Yousef, Emad E H Hassan, Ayman A Amin, Multistage Estimation of the Scale Parameter of Rayleigh Distribution with Simulation, Symmetry (Basel)., vol. 12, pp. 1-14, 2020.

D. Suwarno, Wind speed modeling based on measurement data to predict future wind speed with modified Rayleigh model, Int. J. Power Electron. Drive Syst., 2021.

Hicham Bidaoui, Ikram El Abbassi, Abdelmajid El Bouardi, Abdelmoumen Darcherif., Wind Speed Data Analysis Using Weibull and Rayleigh Distribution Functions, Case Study: Five Cities Northern Morocco, Procedia Manuf., vol. 32, pp. 786-793, 2019.

M. F. Zambak, C. I. Cahyadi, J. Helmi, T. M. Sofie, and S. Suwarno, Evaluation and Analysis of Wind Speed with the Weibull and Rayleigh Distribution Models for Energy Potential Using Three Models, Int. J. Energy Econ. Policy, vol. 13, no. 2, pp. 427-432, Mar. 2023.

Wenxin Wang, Kexin Chen, Yang Bai, Yu Chen, Jianwen Wang., New estimation method of wind power density with three-parameter Weibull distribution: A case on Central Inner Mongolia suburbs, Wind Energy, vol. 25, no. 2, pp. 368-386, 2021.

Suwarno. Ismail Yusuf. M Irwanto. Ayong Hiendro., Analysis of wind speed characteristics using different distribution models in Medan City, Indonesia, Int. J. Power Electron. Drive Syst., vol. 12, no. 2, pp. 1102-1113, 2021.

Konrad Bachanek, Blanka Tundys, Tomasz Wisniewski, Ewa Puzio, Anna Marouskova., Intelligent Street Lighting in a Smart City Concepts-A Direction to Energy Saving in Cities: An Overview and Case Study, Energies, vol. 14, no. 11, pp. 1-19, 2021.


  • There are currently no refbacks.

Please send any question about this web site to
Copyright © 2005-2024 Praise Worthy Prize