Estimation of Energy Yield From Wind Farms Using Artificial Neural Networks

• Published in: IEEE transactions on energy conversion Vol. 24; no. 2; pp. 459 - 464
• Main Authors: Mabel, M.C., Fernandez, E.
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2009; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Artificial neural networks; Energy use; Error analysis; Errors; Humidity; Mathematical model; MATLAB; MATLAB software; Mean square error methods; modeling; Neural networks; Neurons; wind energy estimation; Wind energy generation; Wind farms; Wind power generation; Yield estimation; India, Tamil Nadu; India
• ISSN: 0885-8969; 1558-0059
• DOI: 10.1109/TEC.2008.2001458

This paper uses the data from seven wind farms at Muppandal, Tamil Nadu, India, collected for three years from April 2002 to March 2005 for the estimation of energy yield from wind farms. The model is developed with the help of neural network methodology, and it involves three input variables-wind speed, relative humidity, and generation hours-and one output variable, which give the energy output from wind farms. The modeling is done using MATLAB software. The most appropriate neural network configuration after trial and error is found to be 3-5-1 (3 input layer neurons, 5 hidden layer neurons, 1 output layer neuron). The mean square error for the estimated values with respect to the measured data is 7.6times10 -3 . The results demonstrate that this work is an efficient energy yield estimation tool for wind farms.