Electric load forecasting plays an important role in the planning and operation of the power system for high productivity in any institution of learning. A short-term electrical energy forecast for Gidan Kwano campus, Federal University of Technology Minna, Nigeria was carried out using GMDH-type neural network and the result was compared to that of regression analysis. GMDH-type neural network was used to train and test weekly energy consumed in the campus from September 2010 to December 2014. The neural network was trained using quadratic neural function. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were used as performance indices to test the accuracy of the forecast. The neural network model gave a root mean square error (RMSE) of 0.1189, a mean absolute percentage error (MAPE) of 0.0922 and a correlation (R) value of 0.8995 while the regression analysis method gave a standard error of 10968.1 and a correlation (R) value of 0.1137. Results obtained show the efficacy of the GMDH-type neural network model in forecasting over the regression analysis method.
| Published in | Journal of Electrical and Electronic Engineering (Volume 3, Issue 3) |
| DOI | 10.11648/j.jeee.20150303.14 |
| Page(s) | 42-47 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2015. Published by Science Publishing Group |
Group Method of Data Handling (GMDH), Polynomial Neural Network (PNN), Short Load Term Forecasting (STLF), Mean Absolute Percentage Error (MAPE), Root Mean Square Error (RMSE)
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APA Style
Tsado Jacob, Usman Abraham Usman, Saka Bemdoo, Ajagun Abimbola Susan. (2015). Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network. Journal of Electrical and Electronic Engineering, 3(3), 42-47. https://doi.org/10.11648/j.jeee.20150303.14
ACS Style
Tsado Jacob; Usman Abraham Usman; Saka Bemdoo; Ajagun Abimbola Susan. Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network. J. Electr. Electron. Eng. 2015, 3(3), 42-47. doi: 10.11648/j.jeee.20150303.14
AMA Style
Tsado Jacob, Usman Abraham Usman, Saka Bemdoo, Ajagun Abimbola Susan. Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network. J Electr Electron Eng. 2015;3(3):42-47. doi: 10.11648/j.jeee.20150303.14
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Download@article{10.11648/j.jeee.20150303.14,
author = {Tsado Jacob and Usman Abraham Usman and Saka Bemdoo and Ajagun Abimbola Susan},
title = {Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network},
journal = {Journal of Electrical and Electronic Engineering},
volume = {3},
number = {3},
pages = {42-47},
doi = {10.11648/j.jeee.20150303.14},
url = {https://doi.org/10.11648/j.jeee.20150303.14},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.jeee.20150303.14},
abstract = {Electric load forecasting plays an important role in the planning and operation of the power system for high productivity in any institution of learning. A short-term electrical energy forecast for Gidan Kwano campus, Federal University of Technology Minna, Nigeria was carried out using GMDH-type neural network and the result was compared to that of regression analysis. GMDH-type neural network was used to train and test weekly energy consumed in the campus from September 2010 to December 2014. The neural network was trained using quadratic neural function. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were used as performance indices to test the accuracy of the forecast. The neural network model gave a root mean square error (RMSE) of 0.1189, a mean absolute percentage error (MAPE) of 0.0922 and a correlation (R) value of 0.8995 while the regression analysis method gave a standard error of 10968.1 and a correlation (R) value of 0.1137. Results obtained show the efficacy of the GMDH-type neural network model in forecasting over the regression analysis method.},
year = {2015}
}
TY - JOUR T1 - Short-term Electrical Energy Consumption Forecasting Using GMDH-type Neural Network AU - Tsado Jacob AU - Usman Abraham Usman AU - Saka Bemdoo AU - Ajagun Abimbola Susan Y1 - 2015/05/19 PY - 2015 N1 - https://doi.org/10.11648/j.jeee.20150303.14 DO - 10.11648/j.jeee.20150303.14 T2 - Journal of Electrical and Electronic Engineering JF - Journal of Electrical and Electronic Engineering JO - Journal of Electrical and Electronic Engineering SP - 42 EP - 47 PB - Science Publishing Group SN - 2329-1605 UR - https://doi.org/10.11648/j.jeee.20150303.14 AB - Electric load forecasting plays an important role in the planning and operation of the power system for high productivity in any institution of learning. A short-term electrical energy forecast for Gidan Kwano campus, Federal University of Technology Minna, Nigeria was carried out using GMDH-type neural network and the result was compared to that of regression analysis. GMDH-type neural network was used to train and test weekly energy consumed in the campus from September 2010 to December 2014. The neural network was trained using quadratic neural function. Root mean square error (RMSE) and mean absolute percentage error (MAPE) were used as performance indices to test the accuracy of the forecast. The neural network model gave a root mean square error (RMSE) of 0.1189, a mean absolute percentage error (MAPE) of 0.0922 and a correlation (R) value of 0.8995 while the regression analysis method gave a standard error of 10968.1 and a correlation (R) value of 0.1137. Results obtained show the efficacy of the GMDH-type neural network model in forecasting over the regression analysis method. VL - 3 IS - 3 ER -
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