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An Adaptive Hybrid Soft Computing Approach for Wind Energy Prediction

Published on September 2015 by Smrutirekha Sahoo, Tapaswini Nayak, M.r. Senapati
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International Conference on Emergent Trends in Computing and Communication
Foundation of Computer Science USA
ETCC2015 - Number 2
September 2015
Authors: Smrutirekha Sahoo, Tapaswini Nayak, M.r. Senapati
8b411807-0fe5-4149-bf72-57d57261a4af

Smrutirekha Sahoo, Tapaswini Nayak, M.r. Senapati . An Adaptive Hybrid Soft Computing Approach for Wind Energy Prediction. International Conference on Emergent Trends in Computing and Communication. ETCC2015, 2 (September 2015), 37-42.

@article{
author = { Smrutirekha Sahoo, Tapaswini Nayak, M.r. Senapati },
title = { An Adaptive Hybrid Soft Computing Approach for Wind Energy Prediction },
journal = { International Conference on Emergent Trends in Computing and Communication },
issue_date = { September 2015 },
volume = { ETCC2015 },
number = { 2 },
month = { September },
year = { 2015 },
issn = 0975-8887,
pages = { 37-42 },
numpages = 6,
url = { /proceedings/etcc2015/number2/22342-4573/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Emergent Trends in Computing and Communication
%A Smrutirekha Sahoo
%A Tapaswini Nayak
%A M.r. Senapati
%T An Adaptive Hybrid Soft Computing Approach for Wind Energy Prediction
%J International Conference on Emergent Trends in Computing and Communication
%@ 0975-8887
%V ETCC2015
%N 2
%P 37-42
%D 2015
%I International Journal of Computer Applications
Abstract

The prediction of wind farm output power is considered as an emphatic way to increase the wind energy capacity and improve the safety and economy of the power system. The wind farm output energy depends upon various factors such as wind speed, temperature, etc. , which is difficult to be described by some mathematical expression. This paper introduces a method of wind energy prediction for a wind farm of Vietnam based on historical data of wind speed and environment temperature. Wind energy is free, renewable resource, and non-polluting. This paper consists of the hybridization of the ant colony optimization (ACO), particle swarm optimization (PSO) and Adaline Neural Network (ANN) to predict the hourly wind energy. By applying this hybrid technique over the historical data of wind the MAPE determined is 3. 08%.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Ant Colony Optimization Particle Swarm Optimization Adaline Neural Network Hybrid.