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Wind Speed Prediction of Central Region of Chhattisgarh (India) Using Artificial Neural Network and Multiple Linear Regression Technique: A Comparative Study

Author

Listed:
  • Manoj Verma

    (Chhattisgarh Swami Vivekanand Technical University)

  • Harish Kumar Ghritlahre

    (Chhattisgarh Swami Vivekanand Technical University)

  • Ghrithanchi Chandrakar

    (Chhattisgarh Swami Vivekanand Technical University)

Abstract

There are many renewable energy sources available in the earth like the sun, wind, biomass, tides, heat of the earth etc. Wind energy is one of the important energy sources, which can be used for generating electricity, water pumping, grinding of grains with the use of wind energy conversion system. Wind is generated on the earth surface by uneven heating and it is intermittent in nature. Wind speed is a very important factor and its prediction is very useful for wind power generation and many other purposes. In this study, artificial neural network (ANN) and multiple linear regressions (MLR) techniques are utilized to predict wind speed. For this aim, one year’s data has been taken which is a total of 365 data sets. In both techniques, five parameters were taken which are relative humidity, wind direction, ambient temperature, ambient pressure and perceptible water. In the first ANN technique, an MLP model was developed using these parameters in input layer, with wind speed as output variable. The 5-20-1 neural model has been trained by LM learning algorithm and predicted minimum RMSE and MRE values as 0.4558 and 0.1589 respectively and acceptable value of coefficient of correlation (R) as 0.90162. In the second technique of MLR, same parameters have been used as independent variables and dependent variable. The regression model predicted with coefficient of correlation value as 0.77852. The comparative analysis of performance of both models shows that the MLP model is better than MLR model. Additionally, to find the most sensitive variable, sensitivity analysis has also been done. Perceptible water was found to be the most sensitive variable and the sensitivity sequence of the parameters is: Prw > Ta > WD > Pa > RH.

Suggested Citation

  • Manoj Verma & Harish Kumar Ghritlahre & Ghrithanchi Chandrakar, 2023. "Wind Speed Prediction of Central Region of Chhattisgarh (India) Using Artificial Neural Network and Multiple Linear Regression Technique: A Comparative Study," Annals of Data Science, Springer, vol. 10(4), pages 851-873, August.
    • Handle: RePEc:spr:aodasc:v:10:y:2023:i:4:d:10.1007_s40745-021-00332-1
    DOI: 10.1007/s40745-021-00332-1
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    References listed on IDEAS

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    1. Cao, Qing & Ewing, Bradley T. & Thompson, Mark A., 2012. "Forecasting wind speed with recurrent neural networks," European Journal of Operational Research, Elsevier, vol. 221(1), pages 148-154.
    2. Lei, Ma & Shiyan, Luan & Chuanwen, Jiang & Hongling, Liu & Yan, Zhang, 2009. "A review on the forecasting of wind speed and generated power," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(4), pages 915-920, May.
    3. Philippopoulos, Kostas & Deligiorgi, Despina, 2012. "Application of artificial neural networks for the spatial estimation of wind speed in a coastal region with complex topography," Renewable Energy, Elsevier, vol. 38(1), pages 75-82.
    4. Fadare, D.A., 2010. "The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria," Applied Energy, Elsevier, vol. 87(3), pages 934-942, March.
    5. Monfared, Mohammad & Rastegar, Hasan & Kojabadi, Hossein Madadi, 2009. "A new strategy for wind speed forecasting using artificial intelligent methods," Renewable Energy, Elsevier, vol. 34(3), pages 845-848.
    6. Manish Sharma & Shikha N. Khera & Pritam B. Sharma, 2019. "Applicability of Machine Learning in the Measurement of Emotional Intelligence," Annals of Data Science, Springer, vol. 6(1), pages 179-187, March.
    7. Indranil SenGupta & William Nganje & Erik Hanson, 2021. "Refinements of Barndorff-Nielsen and Shephard Model: An Analysis of Crude Oil Price with Machine Learning," Annals of Data Science, Springer, vol. 8(1), pages 39-55, March.
    8. Cadenas, Erasmo & Rivera, Wilfrido, 2010. "Wind speed forecasting in three different regions of Mexico, using a hybrid ARIMA–ANN model," Renewable Energy, Elsevier, vol. 35(12), pages 2732-2738.
    9. Harish Kumar Ghritlahre & Radha Krishna Prasad, 2018. "Development of Optimal ANN Model to Estimate the Thermal Performance of Roughened Solar Air Heater Using Two different Learning Algorithms," Annals of Data Science, Springer, vol. 5(3), pages 453-467, September.
    10. Çam, Ertugrul & Arcaklıoğlu, Erol & Çavuşoğlu, Abdullah & Akbıyık, Bilge, 2005. "A classification mechanism for determining average wind speed and power in several regions of Turkey using artificial neural networks," Renewable Energy, Elsevier, vol. 30(2), pages 227-239.
    11. James M. Tien, 2017. "Internet of Things, Real-Time Decision Making, and Artificial Intelligence," Annals of Data Science, Springer, vol. 4(2), pages 149-178, June.
    12. Rajsekhar, B. & Van Hulle, F. & Jansen, J. C., 1999. "Indian wind energy programme: performance and future directions," Energy Policy, Elsevier, vol. 27(11), pages 669-678, October.
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