IDEAS home Printed from https://ideas.repec.org/a/spr/aodasc/v10y2023i4d10.1007_s40745-021-00332-1.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s40745-021-00332-1
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.
    File URL: https://libkey.io/10.1007/s40745-021-00332-1?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    3. 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.
    4. 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.
    5. 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.
    6. 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.
    7. 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.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Zhang, Chu & Qiao, Xiujie & Zhang, Zhao & Wang, Yuhan & Fu, Yongyan & Nazir, Muhammad Shahzad & Peng, Tian, 2024. "Simultaneous forecasting of wind speed for multiple stations based on attribute-augmented spatiotemporal graph convolutional network and tree-structured parzen estimator," Energy, Elsevier, vol. 295(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ramasamy, P. & Chandel, S.S. & Yadav, Amit Kumar, 2015. "Wind speed prediction in the mountainous region of India using an artificial neural network model," Renewable Energy, Elsevier, vol. 80(C), pages 338-347.
    2. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
    3. Koo, Junmo & Han, Gwon Deok & Choi, Hyung Jong & Shim, Joon Hyung, 2015. "Wind-speed prediction and analysis based on geological and distance variables using an artificial neural network: A case study in South Korea," Energy, Elsevier, vol. 93(P2), pages 1296-1302.
    4. Jha, Sunil Kr. & Bilalovic, Jasmin & Jha, Anju & Patel, Nilesh & Zhang, Han, 2017. "Renewable energy: Present research and future scope of Artificial Intelligence," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 297-317.
    5. Li, Gong & Shi, Jing, 2010. "On comparing three artificial neural networks for wind speed forecasting," Applied Energy, Elsevier, vol. 87(7), pages 2313-2320, July.
    6. Wang, Jianzhou & Hu, Jianming & Ma, Kailiang & Zhang, Yixin, 2015. "A self-adaptive hybrid approach for wind speed forecasting," Renewable Energy, Elsevier, vol. 78(C), pages 374-385.
    7. Carapellucci, Roberto & Giordano, Lorena, 2013. "The effect of diurnal profile and seasonal wind regime on sizing grid-connected and off-grid wind power plants," Applied Energy, Elsevier, vol. 107(C), pages 364-376.
    8. Sun, Fei & Jin, Tongdan, 2022. "A hybrid approach to multi-step, short-term wind speed forecasting using correlated features," Renewable Energy, Elsevier, vol. 186(C), pages 742-754.
    9. Chen, Kuilin & Yu, Jie, 2014. "Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach," Applied Energy, Elsevier, vol. 113(C), pages 690-705.
    10. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
    11. Wang, Jianzhou & Xiong, Shenghua, 2014. "A hybrid forecasting model based on outlier detection and fuzzy time series – A case study on Hainan wind farm of China," Energy, Elsevier, vol. 76(C), pages 526-541.
    12. Zhang, Wenyu & Wu, Jie & Wang, Jianzhou & Zhao, Weigang & Shen, Lin, 2012. "Performance analysis of four modified approaches for wind speed forecasting," Applied Energy, Elsevier, vol. 99(C), pages 324-333.
    13. Hu, Jianming & Wang, Jianzhou & Xiao, Liqun, 2017. "A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts," Renewable Energy, Elsevier, vol. 114(PB), pages 670-685.
    14. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
    15. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
    16. Prashant Singh & Prashant Verma & Nikhil Singh, 2022. "Offline Signature Verification: An Application of GLCM Features in Machine Learning," Annals of Data Science, Springer, vol. 9(6), pages 1309-1321, December.
    17. Zhao, Yongning & Ye, Lin & Li, Zhi & Song, Xuri & Lang, Yansheng & Su, Jian, 2016. "A novel bidirectional mechanism based on time series model for wind power forecasting," Applied Energy, Elsevier, vol. 177(C), pages 793-803.
    18. Dong, Yunxuan & Wang, Jing & Xiao, Ling & Fu, Tonglin, 2021. "Short-term wind speed time series forecasting based on a hybrid method with multiple objective optimization for non-convex target," Energy, Elsevier, vol. 215(PB).
    19. Zjavka, Ladislav, 2015. "Wind speed forecast correction models using polynomial neural networks," Renewable Energy, Elsevier, vol. 83(C), pages 998-1006.
    20. Tian, Chengshi & Hao, Yan & Hu, Jianming, 2018. "A novel wind speed forecasting system based on hybrid data preprocessing and multi-objective optimization," Applied Energy, Elsevier, vol. 231(C), pages 301-319.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:aodasc:v:10:y:2023:i:4:d:10.1007_s40745-021-00332-1. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.