Abstract
This paper addresses design and development of an artificial neural network based system for prediction of wind energy produced by wind turbines. Now in the last decade, renewable energy emerged as an additional alternative source for electrical power generation. We need to assess wind power generation capacity by wind turbines because of its non-exhaustible nature. The power generation by electric wind turbines depends on the speed of wind, flow direction, fluctuations, density of air, generator hours, seasons of an area, and wind turbine position. During a particular season, wind power generation access can be increased. In such a case, wind energy generation prediction is crucial for transmission of generated wind energy to a power grid system. It is advisable for the wind power generation industry to predict wind power capacity to diagnose it. The present paper proposes an effort to apply artificial neural network technique for measurement of the wind energy generation capacity by wind farms in Harshnath, Sikar, Rajasthan, India.
Highlights
Wind is a pollution-free renewable energy source
The present paper proposes an effort to apply artificial neural network technique for measurement of the wind energy generation capacity by wind farms in Harshnath, Sikar, Rajasthan, India
The wind energy relative accuracy is measured in terms of Mean Squared Error (MSE) and the Mean Absolute Error (MAE)
Summary
Wind is a pollution-free renewable energy source. It is the only natural source of energy available everywhere. Researchers and scientists have developed a number of energy estimation and prediction techniques for wind energy produced by wind farms [5, 6, 12]. In their works, artificial intelligence methods such as neural networks [5] and fuzzy logic are found efficient and accurate in comparison to traditional statistical methods [3, 4, 10,11,12,13,14,15,16,17]. The wind energy relative accuracy is measured in terms of Mean Squared Error (MSE) and the Mean Absolute Error (MAE)
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
