Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine


  •   James Maina Muriithi

  •   Harrison Ngetha

  •   Jean Byiringiro

  •   Kevin Volkmer

  •   Thomas Carolus


Evaluation of the power potential of a particular type of wind turbine at a specific site is necessary for economic decisions. Therefore, the information of a wind turbine and that of a site have to be measured or predicted and then combined with the power curve of a wind turbine. The main objective of this research was to predict the power potential of the existing small wind turbine with a diameter of 3m and the wind turbine site at the University of Siegen and compare with the annual energy calculated from the measured one year of wind and turbine data. Techniques for prediction of the wind speed distribution of a site were determined and modeled. The power curve of the wind turbine was modeled from data recorded by applying a technique from the novel methods for modelling the power curve. In this research, artificial neural network, Weibull and Rayleigh are the techniques modeled to predict wind speed distribution at the wind turbine site. Rayleigh and Weibull were chosen since the two models depict a better wind speed distribution and require the mean and the standard deviation of the wind speed at the wind turbine site. A neural network trained with the backward propagation levernberg-Marquardt algorithm was applied to predict the wind speed and power potential of the wind turbine site. A comparison between Weibull, Rayleigh and the Levernberg-Marquardt trained neural network wind speed was made. The power curve of the wind turbine was successfully evaluated from wind data and wind turbine data recorded. The results indicate that the annual mean wind speed of the region is 2.54 (m/s) and about 20% of the wind availability was blowing from the west. The annual energy yield predicted from the trained neural network was 372 (kWh) closer to that determined from measured wind speed 360 (kWh) than that determined from Weibull and Rayleigh 337 and 233 (kWh) respectively. The three prediction models are applicable in any region to predict the annual energy of a particular wind turbine site with minimal data available.

Keywords: Rayleigh, Weibull, Artificial Neural Network, Power Curve, Annual Energy Prediction


N. L. Panwar, S. C. Kaushik, and S. Kothari, “Role of renewable energy sources in environmental protection: A review,” Renewable and Sustainable Energy Reviews, vol. 15, no. 3, pp. 1513–1524, 2011.

R. Gnatowska and E. Moryń-Kucharczyk, “Current status of wind energy policy in Poland,” Renewable Energy, vol. 135, pp. 232–237, 2019.

S. Mathew, “Analysis of wind regimes for energy estimation,” Renewable Energy, vol. 25; Jg. 2002-03-01, no. 3, pp. 381–399, 2002.

L. Mba, P. Meukam, and A. Kemajou, “Application of artificial neural network for predicting hourly indoor air temperature and relative humidity in modern building in humid region,” Energy and Buildings, vol. 121, pp. 32–42, 2016.

J. V. Tu, “Advantages and disadvantages of using artificial neural networks versus logistic regression for predicting medical outcomes,” Journal of Clinical Epidemiology, vol. 49, no. 11, pp. 1996.

S. K. Jha and J. Bilalovikj, “Short-term wind speed prediction at Bogdanci power plant in FYROM using an artificial neural network,” International Journal of Sustainable Energy, vol. 2014, pp. 1–16, 2018.

S. Makridakis, E. Spiliotis, and V. Assimakopoulos, “Statistical and Machine Learning forecasting methods: Concerns and ways forward.” (eng), PloS one, vol. 13, no. 3, e0194889, 2018.

B. Safari and J. Gasore, “A statistical investigation of wind characteristics and wind energy potential based on the Weibull and Rayleigh models in Rwanda,” Renewable Energy, vol. 35, no. 12, pp. 2874–2880, 2010.

J. F. Manwell, J. G. McGowan, and A. L. Rogers, Wind energy explained: Theory, design and application / J.F. Manwell and J.G. McGowan, Anthony Rogers, 2nd ed. Chichester: John Wiley, 2009.

Power performance measurements of electricity producing wind turbines, 860, 2017.

T. Gerhard, S. Michael, and T. Carolus, “Small Horizontal Axis Wind Turbine: Analytical blade design and comparison with a Rans-Prediction and First experimental data,” Universität Siegen, 2013.

K. Volkmer and T. Adam, “The wind Turbine of the University of Siegen,” Institut für Fluid- und Thermodynamik, Feb. 2016.

D. H. TDidane, N. Rosly, M. F. Zulkafli, and S. S. Shamsudin, “Evaluation of Wind Energy Potential as a Power Generation Source in Chad,” International Journal of Rotating Machinery, vol. 2017, no. 1, pp. 1–10, 2017.

M. Bassyouni et al., “Assessment and Analysis of Wind Power Resource Using Weibull Parameters,” Energy Exploration & Exploitation, vol. 33, no. 1, pp. 105–122, 2015.

F. A. L. Jowder, “Weibull and Rayleigh Distribution Functions of Wind Speeds in Kingdom of Bahrain,” Wind Engineering, vol. 30, no. 5, pp. 439–445, 2006.

A. David, “Rayleigh Distribution-Based Model for Prediction of Wind Energy Potential of Cameroon,” Energy Review, vol. 1, no. 1, pp. 26–43, 2014.

B. Hrafnkelsson, G. Oddsson, and R. Unnthorsson, “A Method for Estimating Annual Energy Production Using Monte Carlo Wind Speed Simulation,” Energies, vol. 9, no. 4, p. 286, 2016.

R. Gasch and J. Twele, Wind power plants: Fundamentals, design, construction and operation / [edited by] R. Gasch, J. Twele. Berlin: Solarpraxis; London : James & James, 2002.

E. H. Lysen, “Introduction to Wind Energy: wind Energy development countries SWD,” 1983.

S. Nasiru, “Serial Weibull Rayleigh distribution: theory and application,” IJCSM, vol. 7, no. 3, p. 239, 2016.

I. Tizgui, F. El Guezar, H. Bouzahir, and B. Benaid, “Comparison of methods in estimating Weibull parameters for wind energy applications,” Int J of Energy Sector Man, vol. 11, no. 4, pp. 650–663, 2017.

S. Ashok, “Optimised model for community-based hybrid energy system,” Renewable Energy, vol. 32, no. 7, pp. 1155–1164, 2007.

A. Kusiak, H. Zheng, and Z. Song, “On-line monitoring of power curves,” Renewable Energy, vol. 34, no. 6, pp. 1487–1493, 2009.


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How to Cite
Muriithi, J., Ngetha, H., Byiringiro, J., Volkmer, K. and Carolus, T. 2019. Wind Data Analysis, Annual Resource Estimation And Comparison With Measured Annual Energy Yield at the University Wind Turbine. European Journal of Engineering and Technology Research. 4, 6 (Jun. 2019), 25-33. DOI: