Document Type
Article
Publication Date
2015
Publication Title
Journal of Power Technologies
Abstract
Wind energy is ideally suited for distributed generation systems to meet growing demand for electricity that find applications especially in developing countries. The motive of this study is to develop an efficient method to help identify and select the best sites to harvest the wind energy in Egypt. In this paper, a novel approach is proposed to estimate and appraise wind energy resources using Artificial Neural Network (ANN). To achieve this goal, an ANN-based algorithm was created and trained using relevant data collected from several wind monitoring posts installed across the country. Parameters such as latitude, longitude, elevation, and monthly wind speed were recorded for use as inputs and outputs for the ANN system. A key advantage of this model lies in its ability to predict and make interpolation between the learning curves data without the need for additional training runs. This feature was attainable using back-propagation techniques to estimate the model parameters with the aid of MATLAB. Another advantage of this proposed model is the derivation of closed-form input/output relationships which permitted to obtain fast and accurate results with excellent regression factors. Simulation results were presented in 3D plots and validated with real system data. Finally, The Horizontal Axis Wind Turbine (HWT) is modeled by many actual data from various manufacturers’ manuals. Results have shown that the actual data was closely matched confirming the merits of this proposed model. Many other desirable features that researchers can find useful to quantify wind energy resources such as easy model construction, integration with other technologies, and converting into Visual Basic or C++ codes were also identified with this model.
Recommended Citation
El-Shahat, Adel, Rami J. Haddad, Youakim Kalaani.
2015.
"Wind Energy Estimation Functions for Future Homes."
Journal of Power Technologies, 95 (3).
source: http://papers.itc.pw.edu.pl/index.php/JPT/article/download/605/664
https://digitalcommons.georgiasouthern.edu/electrical-eng-facpubs/19
Comments
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal. Article obtained from Journal of Power Technologies.