Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk

Teuku Multazam; Ratna Ika Putri; Margo Pujiantara; Vita Lystianingrum; Ardyono Priyadi; P. Hery Mauridhi

doi:10.1109/ICICI-BME.2017.8537769

Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk

Teuku Multazam, Ratna Ika Putri, Margo Pujiantara, Vita Lystianingrum, Ardyono Priyadi, P. Hery Mauridhi

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

10 Citations (Scopus)

Abstract

Wind energy is one of the main sources of renewable energy that is widely converted to electrical energy because reduce emission. The fluctuating of the wind will affect power quality of wind power generation when delivered to grid so that it takes prediction of short-term wind speed. In this study, wind speed prediction use Backpropagation Neural Network (BPNN) with Lavenberg - Marquardt algorithms for weight update. Its performance will be compared with Conjugate Gradient Fletcher Reeves (CGFR) and Conjugate Gradient Quasi Newton (BFGS) based on correlation coefficient, Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE) and computation time. Based on the testing, Levenberg Marquardt Backpropagation Neural Network is the most optimal algorithm compared to fletcher - reeves and quasi newton where the value of correlation coefficient is 0.99056, MSE 0.0187, MAPE 5.1965 and computation time 79.19 milisecon.

Original language	English
Title of host publication	Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	163-166
Number of pages	4
ISBN (Electronic)	9781538634554
DOIs	https://doi.org/10.1109/ICICI-BME.2017.8537769
Publication status	Published - 15 Nov 2018
Event	5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017 - Bandung, Indonesia Duration: 6 Nov 2017 → 7 Nov 2017

Publication series

Name	Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017

Conference

Conference	5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017
Country/Territory	Indonesia
City	Bandung
Period	6/11/17 → 7/11/17

Keywords

BPNN
Wind Turbine
algorithm
prediction short term
wind speed

UN SDGs

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

Access to Document

10.1109/ICICI-BME.2017.8537769

Cite this

Multazam, T., Putri, R. I., Pujiantara, M., Lystianingrum, V., Priyadi, A., & Hery Mauridhi, P. (2018). Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk. In Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017 (pp. 163-166). Article 8537769 (Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICICI-BME.2017.8537769

Multazam, Teuku ; Putri, Ratna Ika ; Pujiantara, Margo et al. / Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk. Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017. Institute of Electrical and Electronics Engineers Inc., 2018. pp. 163-166 (Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017).

@inproceedings{3b1df2504315448f9ffda17ca8516297,

title = "Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk",

abstract = "Wind energy is one of the main sources of renewable energy that is widely converted to electrical energy because reduce emission. The fluctuating of the wind will affect power quality of wind power generation when delivered to grid so that it takes prediction of short-term wind speed. In this study, wind speed prediction use Backpropagation Neural Network (BPNN) with Lavenberg - Marquardt algorithms for weight update. Its performance will be compared with Conjugate Gradient Fletcher Reeves (CGFR) and Conjugate Gradient Quasi Newton (BFGS) based on correlation coefficient, Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE) and computation time. Based on the testing, Levenberg Marquardt Backpropagation Neural Network is the most optimal algorithm compared to fletcher - reeves and quasi newton where the value of correlation coefficient is 0.99056, MSE 0.0187, MAPE 5.1965 and computation time 79.19 milisecon.",

keywords = "BPNN, Wind Turbine, algorithm, prediction short term, wind speed",

author = "Teuku Multazam and Putri, {Ratna Ika} and Margo Pujiantara and Vita Lystianingrum and Ardyono Priyadi and {Hery Mauridhi}, P.",

note = "Publisher Copyright: {\textcopyright} 2017 IEEE.; 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017 ; Conference date: 06-11-2017 Through 07-11-2017",

year = "2018",

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Multazam, T, Putri, RI, Pujiantara, M , Lystianingrum, V , Priyadi, A & Hery Mauridhi, P 2018, Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk. in Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017., 8537769, Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017, Institute of Electrical and Electronics Engineers Inc., pp. 163-166, 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017, Bandung, Indonesia, 6/11/17. https://doi.org/10.1109/ICICI-BME.2017.8537769

Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk. / Multazam, Teuku; Putri, Ratna Ika; Pujiantara, Margo et al.
Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017. Institute of Electrical and Electronics Engineers Inc., 2018. p. 163-166 8537769 (Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017).

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

TY - GEN

T1 - Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk

AU - Multazam, Teuku

AU - Putri, Ratna Ika

AU - Pujiantara, Margo

AU - Lystianingrum, Vita

AU - Priyadi, Ardyono

AU - Hery Mauridhi, P.

PY - 2018/11/15

Y1 - 2018/11/15

N2 - Wind energy is one of the main sources of renewable energy that is widely converted to electrical energy because reduce emission. The fluctuating of the wind will affect power quality of wind power generation when delivered to grid so that it takes prediction of short-term wind speed. In this study, wind speed prediction use Backpropagation Neural Network (BPNN) with Lavenberg - Marquardt algorithms for weight update. Its performance will be compared with Conjugate Gradient Fletcher Reeves (CGFR) and Conjugate Gradient Quasi Newton (BFGS) based on correlation coefficient, Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE) and computation time. Based on the testing, Levenberg Marquardt Backpropagation Neural Network is the most optimal algorithm compared to fletcher - reeves and quasi newton where the value of correlation coefficient is 0.99056, MSE 0.0187, MAPE 5.1965 and computation time 79.19 milisecon.

AB - Wind energy is one of the main sources of renewable energy that is widely converted to electrical energy because reduce emission. The fluctuating of the wind will affect power quality of wind power generation when delivered to grid so that it takes prediction of short-term wind speed. In this study, wind speed prediction use Backpropagation Neural Network (BPNN) with Lavenberg - Marquardt algorithms for weight update. Its performance will be compared with Conjugate Gradient Fletcher Reeves (CGFR) and Conjugate Gradient Quasi Newton (BFGS) based on correlation coefficient, Mean Square Error (MSE), Mean Absolute Percentage Error (MAPE) and computation time. Based on the testing, Levenberg Marquardt Backpropagation Neural Network is the most optimal algorithm compared to fletcher - reeves and quasi newton where the value of correlation coefficient is 0.99056, MSE 0.0187, MAPE 5.1965 and computation time 79.19 milisecon.

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KW - wind speed

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BT - Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017

PB - Institute of Electrical and Electronics Engineers Inc.

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Multazam T, Putri RI, Pujiantara M , Lystianingrum V , Priyadi A , Hery Mauridhi P. Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk. In Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017. Institute of Electrical and Electronics Engineers Inc. 2018. p. 163-166. 8537769. (Proceedings of 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering, ICICI-BME 2017). doi: 10.1109/ICICI-BME.2017.8537769

Short-Term Wind Speed Prediction Base on Backpropagation Levenberg-Marquardt Algorithm; Case Study Area Nganjuk

Abstract

Publication series

Conference

Keywords

UN SDGs

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