Estimating the energy production of the wind turbine using artificial neural network. Issue 5 (July 2016)

• Record Type: Journal Article
• Authors: Mert, İlker; Karakuş, Cuma; Üneş, Fatih
• Is Part Of: Neural computing & applications. Volume 27:Issue 5(2016)
• Page Start: 1231
• Page End: 1244
• Publication Date: 2016-07
• Subjects: Artificial neural networks -- Wind energy -- Stepwise multilinear regression -- Bayesian regularization -- Levenberg–Marquardt -- Resilient back-propagation; Neural networks (Computer science) -- Periodicals; Neural circuitry -- Periodicals; Artificial intelligence -- Periodicals; Neural Networks (Computer) -- Periodicals; Réseaux neuronaux (Informatique) -- Périodiques; Réseaux nerveux -- Périodiques; Intelligence artificielle -- Périodiques; 006.32
• DOI: 10.1007/s00521-015-1921-0
• Languages: English
• ISSNs: 0941-0643

Abstract

Abstract Due to fluctuating weather conditions, estimating wind energy potential is still a significant problem. Artificial neural networks (ANNs) have been commonly used in short-term and just-in-time modeling of wind power generation systems based on main weather parameters such as wind speed, temperature, and humidity. Two different datasets called hourly main weather data (MWD) and daily sub-data (DSD) are used to estimate a wind turbine power generation in this study. MWD are based on historically observed wind speed, wind direction, air temperature, and pressure parameters. Besides, DSD created with statistical terms of MWD consist of maximum, minimum, mean, standard deviation, skewness, and kurtosis values. The main purpose of this study in particular was to develop a multilinear model representing the relationship between the DSD with the calculated minimum (P min ) and maximum (P max ) power generation values as well as the total power generation (P sum ) produced in a day by a wind turbine based on the MWD. While simulation values of the turbine, P min, P max, andP sum, were used as the separately dependent parameters, DSD were determined as independent parameters in the estimation models. Stepwise regression was used to determine efficient independent parameters on the dependent parameters and to remove the inefficient parameters in the exploratory phase of study. These efficient parameters and simulated power generation values were used for training and testing the developed ANN models. Accuracy test results show that interoperability framework models based on stepwise regression and the neural network models are more accurate and more reliable than a linear approach.