Estimation of Energy Yield From Wind Farms Using Artificial Neural Networks

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.

I. Introduction

Estimation of wind power generation can be considered as a more efficient way to increase the wind energy penetration. While conventional power plants produce a constant power output, the output of a wind power plant fluctuates. In order to successfully integrate wind energy with traditional power generation, it is necessary to have the ability to forecast the available energy yield of a wind farm for a given period. Accurate forecasts of wind energy resources are critical for economic viability, system reliability, scheduling, and long-range planning [1]. The estimation of wind power generation is generally comprised of several modeling techniques that combine meteorological and historical generation data [2]–[4]. In order to achieve the highest possible accuracy, the methods should also incorporate appropriate parameter and data.