This repository provides the source code for the visualizations and calculations in the research paper Wind Energy : A Practical Power Analysis Approach.
Paper Title : Wind Energy: A Practical Power Analysis Approach
Publisher : IEEE (The Institute of Electrical and Electronics Engineers)
Authors :
- Farhan Hai Khan
- Tannistha Pal
- Rounik Roy
- Bishal Kundu
DOI : 10.1109/IEMRE52042.2021.9386754
Keywords : Industries, Wind energy, Wind speed, Green products, Wind power generation, Wind turbines, Mathematical model, Wind Power Analysis, Renewable Energy, Sustainable Development, Lasso Regression, Instantaneous Active Wind Power, Weibull Distribution, Frequency Distribution of Wind Speed
Published in : 2021 Innovations in Energy Management and Renewable Resources(52042)
Date of Conference : 5-7 Feb. 2021
Date Added to IEEE Xplore : 30 March 2021
Conference Location : Kolkata, India
Abstract : Wind energy is one of the fastest-growing green technologies as it provides clean, safe, and renewable electricity generation. This study provides insights into the available methodologies for sustainable power harnessing using wind resources, scrutinizing the developments in the recent decades and the future potential of global wind power industries. Contrasting and comparing the expected Weibull distribution to the true frequency distribution of the actual wind speed, this research proposes an empirical equation using Polynomial Lasso Regression techniques over field data obtained from Turkey, for the instantaneous active power generated by wind turbines in practical scenarios. A detailed overview of the wind energy calculation and improvements is discussed with the existing wind power production techniques. Finally, an optimal analysis of wind power utilization in top Indian states, and inspection of potential wind power applications is carried out with respect to the Indian subcontinent.
View on :
- IEEE Xplore : https://ieeexplore.ieee.org/document/9386754
- ResearchGate : https://www.researchgate.net/publication/350503721_Wind_Energy_A_Practical_Power_Analysis_Approach
© All rights reserved.
The research is generalized and needs the following common Machine Learning Libraries:
numpy
pandas
matplotlib
seaborn
scikit-learn
ipykernel
All the Jupyter Notebooks were run on Google Colaboratory with the Python 3.7 Language.
We acknowledge Chittal Patel on Kaggle for providing the basic code that inspired this research.
All other references have been mentioned in the original research paper.