Short term wind speed forecasting using artificial neural network: A case study

Hasan Masrur; Meas Nimol; Mohammad Faisal; Golam Mostafa

doi:10.1109/ICISET.2016.7856485

Short term wind speed forecasting using artificial neural network: A case study

Hasan Masrur
, Meas Nimol
, Mohammad Faisal
, Golam Mostafa

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

14 Scopus citations

Abstract

The intermittency of wind speed is very challenging in order to produce wind energy via wind turbine to synchronize with the power system. The accurate wind forecasting models are very important for effective power system management. There are many ways have been introduced for short term accurate wind forecasting. In this paper, Artificial Neural Network (ANN) is used with feed forward back propagation algorithm to forecast short-Term wind speed of Asian Institute of Technology (AIT). After simulating the model in MATLAB, the result shows that the mean absolute percentage error (MAPE) between the predicted and measured wind speed is quite low and noteworthy. It represents the high prediction correctness of short-Term wind speed forecasting using the above mentioned model.

Original language	English
Title of host publication	2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781509061228
DOIs	https://doi.org/10.1109/ICISET.2016.7856485
State	Published - 14 Feb 2017
Externally published	Yes

Publication series

Name	2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016

Bibliographical note

Publisher Copyright:
© 2016 IEEE.

Keywords

Component
Formatting
Insert (key words)
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Styling

ASJC Scopus subject areas

Hardware and Architecture
Energy Engineering and Power Technology
Electrical and Electronic Engineering
Artificial Intelligence
Computer Science Applications

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1109/ICISET.2016.7856485

Cite this

Masrur, H., Nimol, M., Faisal, M., & Mostafa, G. (2017). Short term wind speed forecasting using artificial neural network: A case study. In 2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016 Article 7856485 (2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICISET.2016.7856485

Masrur, Hasan ; Nimol, Meas ; Faisal, Mohammad et al. / Short term wind speed forecasting using artificial neural network : A case study. 2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016. Institute of Electrical and Electronics Engineers Inc., 2017. (2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016).

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abstract = "The intermittency of wind speed is very challenging in order to produce wind energy via wind turbine to synchronize with the power system. The accurate wind forecasting models are very important for effective power system management. There are many ways have been introduced for short term accurate wind forecasting. In this paper, Artificial Neural Network (ANN) is used with feed forward back propagation algorithm to forecast short-Term wind speed of Asian Institute of Technology (AIT). After simulating the model in MATLAB, the result shows that the mean absolute percentage error (MAPE) between the predicted and measured wind speed is quite low and noteworthy. It represents the high prediction correctness of short-Term wind speed forecasting using the above mentioned model.",

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Masrur, H, Nimol, M, Faisal, M & Mostafa, G 2017, Short term wind speed forecasting using artificial neural network: A case study. in 2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016., 7856485, 2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016, Institute of Electrical and Electronics Engineers Inc. https://doi.org/10.1109/ICISET.2016.7856485

Short term wind speed forecasting using artificial neural network: A case study. / Masrur, Hasan; Nimol, Meas; Faisal, Mohammad et al.
2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016. Institute of Electrical and Electronics Engineers Inc., 2017. 7856485 (2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Nimol, Meas

AU - Faisal, Mohammad

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AB - The intermittency of wind speed is very challenging in order to produce wind energy via wind turbine to synchronize with the power system. The accurate wind forecasting models are very important for effective power system management. There are many ways have been introduced for short term accurate wind forecasting. In this paper, Artificial Neural Network (ANN) is used with feed forward back propagation algorithm to forecast short-Term wind speed of Asian Institute of Technology (AIT). After simulating the model in MATLAB, the result shows that the mean absolute percentage error (MAPE) between the predicted and measured wind speed is quite low and noteworthy. It represents the high prediction correctness of short-Term wind speed forecasting using the above mentioned model.

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Masrur H, Nimol M, Faisal M, Mostafa G. Short term wind speed forecasting using artificial neural network: A case study. In 2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016. Institute of Electrical and Electronics Engineers Inc. 2017. 7856485. (2016 International Conference on Innovations in Science, Engineering and Technology, ICISET 2016). doi: 10.1109/ICISET.2016.7856485

Short term wind speed forecasting using artificial neural network: A case study

Abstract

Publication series

Bibliographical note

Keywords

ASJC Scopus subject areas

UN SDGs

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Cite this