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Wind energy forecasting using artificial neural network in himalayan region

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Abstract

The future depends upon the wind energy for the generation of electrical energy. The availability of the wind is either in the coastal areas or in the mountain areas at a suitable altitude. The present research is focused on wind energy availability in the mountain areas. For this purpose, a site has been selected for data collection of the wind in the Himalayan Range called Pir Panjal Range and at a height of 1200 m from the sea level. The sites which have been selected are in such ways that there is no obstruction offered to the wind flow. These are naturally occurring sites and purposed to be suitable for wind energy extraction, which is also an objective of the present research. The data is collected as wind speed, temperature and air density which is used to forecast the wind energy. The forecasting helps in maintain the reliability of electricity at the output. It also gives us the future prospects of wind energy and generation through it in near future; so that, the required planning can be done to improve the reliability of the system. The Artificial Neural Network (ANN) has been used to forecast wind energy. The ANN algorithm, which is used in this research work, uses 30 days data of wind speed, temperature and air density for training purpose and forecasting of the wind energy. The programming has been done in MATLAB environment, whereas the results have been evaluated and compared with actual data for validation.

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Acknowledgement

The author wants to acknowledge the Department of Electrical Engineering, BGSB University by helping and making this research successful. I also acknowledge the students of Batch 2013 of Electrical Engineering Department for their support in collecting the useful data.

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  1. Department of Electrical Engineering, BGSB University, Rajouri, Jammu and Kashmir, India

    Vinod Puri & Nikhil Kumar

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  1. Vinod Puri
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  2. Nikhil Kumar
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Correspondence to Vinod Puri.

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Puri, V., Kumar, N. Wind energy forecasting using artificial neural network in himalayan region. Model. Earth Syst. Environ. 8, 59–68 (2022). https://doi.org/10.1007/s40808-020-01070-8

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  • DOI: https://doi.org/10.1007/s40808-020-01070-8

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