Wind speed prediction in the mountainous region of India using an artificial neural network model. (August 2015)

• Record Type: Journal Article
• Authors: Ramasamy, P.; Chandel, S.S.; Yadav, Amit Kumar
• Is Part Of: Renewable energy. Volume 80(2015)
• Page Start: 338
• Page End: 347
• Publication Date: 2015-08
• Subjects: India -- Western Himalayas -- Wind speed prediction -- Artificial neural network -- Power generation; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.renene.2015.02.034
• Languages: English
• ISSNs: 0960-1481

Abstract

Abstract: Measured wind speed data are not available for most sites in the mountainous regions of India. The objective of present study is to predict wind speeds for 11 locations in the Western Himalayan Indian state of Himachal Pradesh to identify possible wind energy applications. An artificial neural network (ANN) model is used to predict wind speeds using measured wind data of Hamirpur location for training and testing. Temperature, air pressure, solar radiation and altitude are taken as inputs for the ANN model to predict daily mean wind speeds. Mean absolute percentage error (MAPE) and correlation coefficient between the predicted and measured wind speeds are found to be 4.55% and 0.98 respectively. Predicted wind speeds are found to range from 1.27 to 3.78 m/s for Bilaspur, Chamba, Kangra, Kinnaur, Kullu, Keylong, Mandi, Shimla, Sirmaur, Solan and Una locations. A micro-wind turbine is used to assess the wind power generated at these locations which is found to vary from 773.61 W to 5329.76 W which is suitable for small lighting applications. Model is validated by predicting wind speeds for Gurgaon city for which measured data are available with MAPE 6.489% and correlation coefficient 0.99 showing high prediction accuracy of the developed ANN Model. Graphical abstract: Highlights: ANN model is used for wind speed prediction of 11 mountain locations in India. Temperature, pressure, solar radiation, altitude are used as inputs to ANN model. MAPE between predicted and measured wind speed is found to be 4.55%. Suitability of power generation by a micro wind turbine is assessed.