Wind energy assessment and mapping using terrain nonlinear autoregressive neural network (TNARX) and wind station data

• Main Authors: Salisu Muhammad Lawan, Wan Azlan Wan Zainal Abidin
• Format: Article
• Language: English
• Published: Taylor & Francis Group 2018-01-01
• Series: Cogent Engineering
• Subjects: wind; wind speed; wind power; artificial neural network; sarawak; bintulu
• Online Access: http://dx.doi.org/10.1080/23311916.2018.1452594

This paper presents the potential of generating wind power using soft computing model and ground station data. In reality, the process of wind resource assessment is to set up an experiment in the targeted locations, and measure the wind speed and direction. In this paper, a prediction model based on the terrain based neural network named terrain nonlinear autoregressive neural network (TNARX) is proposed to forecast the wind speed in the areas not covered by measurements using a ground station located nearby. The model has meteorological, physical and topographical as input, while the wind speed is the target variable. The suitability of the proposed model was judged using statistical measures. The paper shows characteristics of wind speed and the most prevailing wind directions. The variation of wind speed at 10–40 m heights was obtained and presented. Wind speed distribution modelling was carried out using five statistical models. It was found that Weibull and Gamma fits the wind speed of the studied areas. Wind power and energy density results show the areas falls within class 1, which is possible for harnessing energy content in wind for small scale purposes.