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), 2024. 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)
| [1] | A.Indira , M. Prakash, S. Pradhan, S.S. Thakur and D.V. Rajan (2014). “Short-term load forecasting of an Interconnected Grid using Neural Network”. American Journal of Engineering Research (AJER), e-ISSN: 2320-0847, p-ISSN: 2320-0936, volume-03, Issue-04, pp-271-280. |
| [2] | Seyed-Masoud Barakati, Ali Akbar Gharaveisi and Seyed Mohammad Reza Rafiei (2015). “Short-term load forecasting using mixed lazy learning method”. Turkish Journal of Electrical Engineering & Computer Sciences, Turk J Elect Eng & Comp Sci (2015) 23: 201-211, doi: 10.3906/elk-1301-134. |
| [3] | Feinberg, E.A. and Genethliou D., (2005). Load Forecasting in: Applied Mathematics for Power Systems. State University of New York, Stony Brook, Chapter 12. |
| [4] | Isaac, A.S., Felly- Njoku, C.F., Adewale, A.A. and Ayokunle A.A., (2014). Medium-term load forecasting of Covenant University using the Regression analysis method. Journal of Energy Technologies and Policy, ISSN 2224-3232 (paper), ISSN 2225-0573 (online), vol. 4, No. 4, 2014. |
| [5] | Simaneka, A., (2008). Development of models for short-term load forecasting using Artificial Neural Network. Master’s Thesis, Faculty of Engineering, Cape Peninsula University of Technology, November 2008. |
| [6] | Bougaardt, G., (2002). An Investigation into the application of Artificial Neural Networks and Cluster Analysis in Long-term load Forecasting. Master’s Thesis, Department of Electrical and Electronic Engineering, University of Cape Town, 1st January, 2002. |
| [7] | Sanjoy Das (1995). “The Polynomial Neural Network”, University of Carlifonia, California 94720 1995. Information Sciences 87, 231-246 (1995), SSDI 0020-0255 (95) 00133-A. |
| [8] | Ivan Galkin, U. Mass Lowell. “Crash Introduction to Artificial Neural Networks”. Materials for UML 91.550 Data Mining Course. |
| [9] | E. Gomez-Ramirez, K. Najim and E. Ikonen (2007). “Forecasting time series with a new architecture for polynomial artificial neural network”. Applied Soft Computing 7 (2007) 1209-1216. |
| [10] | O.A Koshulko and G.A Koshulko (2011). “Validation Strategy in combinatorial and multilayered iterative GMHD Algorithm”. The 4th International Workshop on Inductive Modelling IWIM 2011. |
| [11] | Bon-Gil Koo, Sang-Wook Lee, Wook Kim and June Ho Park (2014). “Comparative Study of Short-term Electric Load Forecasting”. 2014 Fifth International Conference on Intelligent Systems, Modeling and Simulation. |
| [12] | Bon-Gil Koo, Heung-Seok Lee and June Ho Park (2015). “Short-term electric load forecasting based on wavelet transform and GMDH”. J Electr Eng Technol. 2015; 10(?): 30-40, ISSN (Print) 1975-0102, ISSN (online) 2093-7423, http://dx.doi.org/10.5370/JEET.2015.10.2.030 |
| [13] | Huseynov A.F, Yusifbeyli N.A and Hashimov A.M (2010). “Electrical System Load forecasting with Polynomial Neural Networks (based on Combinatorial Algorithm”. Modern Electric Power Systems 2010, Wroclaw, Poland, MEPS’10-paper 04.3 |
| [14] | Francisco Herreŕa Fernández and Fidel Hernández Lozano (2010). “GMDH Algorithm Implemented in Intelligent Identification of a Bioprocess”. ABCM Symposium series in Mechatronics, vol. 4-pp 278-287. |
| [15] | Saeed Fallahi, Meysam Shaverdi and Vahab Bashiri (2011). “Applying GMDH-type Neural Network and Genetic Algorithm for stock price prediction of Iranian cement sector”. Applications and Applied Mathematics: An International Journal (AAM), vol. 6, Issue 2 (December 2011), pp 572-591, ISSN: 1932-9466 |
| [16] | Samsher, K.S. and Unde, M.G., (2012). Short-term forecasting using ANN technique. International Journal of Engineering Sciences and Engineering Technologies, Feb. 2012, ISSN: 2231-6604, volume 1, issue 2, pp: 97-107 © IJSEST |
| [17] | Sanjib, M., (2008). Short-term load forecasting using computational intelligence method. Master’s Thesis, Electronics and Communication Engineering (Specialization in Telematics and signal processing), National Institute of Technology, Rourkela, 2008. |
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
Share This Article
Twitter
Linked In
Facebook
Copy
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 -
Information
Email: