Analysis of wind power generation and prediction using ANN: A case study

• Published in: Renewable energy Vol. 33; no. 5; pp. 986 - 992
• Main Authors: Carolin Mabel, M., Fernandez, E.
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 01.05.2008; Elsevier Science
• Subjects: Applied sciences; Artificial neural networks; Economic data; Electric energy; Energy; Energy economics; Exact sciences and technology; General, economic and professional studies; MATLAB toolbox; Modeling; Natural energy; Wind energy; Wind power prediction; Wind speed; Asia; India; India, Tamil Nadu; Wind power prediction; Wind speed; MATLAB toolbox; Modeling; Artificial neural networks; Relative humidity; Operating time; Windfarm; Wind velocity; Neural network; Monthly variation; Case study; Wind energy; Production capacity; Electric power production
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2007.06.013

Many developing nations, such as India have embarked upon wind energy programs for areas experiencing high average wind speeds throughout the year. One of the states in India that is actively pursuing wind power generation programs is Tamil Nadu. Within this state, Muppandal area is one of the identified regions where wind farm concentration is high. Wind energy engineers are interested in studies that aim at assessing the output of wind farms, for which, artificial intelligence techniques can be usefully adapted. The present paper attempts to apply this concept for assessment of the wind energy output of wind farms in Muppandal, Tamil Nadu (India). Field data are collected from seven wind farms at this site over a period of 3 years from April 2002 to March 2005 and used for the analysis and prediction of power generation from wind farms. The model has been developed with the help of neural network methodology. It involves three input variables—wind speed, relative humidity and generation hours and one output variable-energy output of wind farms. The modeling is done using MATLAB toolbox. The model accuracy is evaluated by comparing the simulated results with the actual measured values at the wind farms and is found to be in good agreement.