This study deals with estimation of electricity demand of Iran on the basis of economic criteria using a genetic-based approach called Gene Expression Programming (GEP) as an expression-driven approach. The GEP-based mathematical model is provided based on population, gross domestic product, exports, and imports. The proposed model is derived based on available data obtained from 1992 to 2006. To assess the forecasting accuracy of the model, the electricity demand from 2007 until 2012 are calculated by the GEP-based model and the obtained results are compared with the real data during this period. To show the accuracy of the model, the results obtained by GEP model are compared with the results obtained from Multi-Layer Perceptron (MLP) neural network and Multiple Linear Regression (MLR) as the two conventional methods. In addition, a five-fold cross-validation and future year prediction are used to show the robustness of the model in predicting the electricity demand. Future estimation of Iran's electric energy consumption is then projected up to 2030 according to three different scenarios. Finally, a sensitivity analysis is conducted to identify the most important independent variables affecting electricity demand.
| Published in | American Journal of Data Mining and Knowledge Discovery (Volume 1, Issue 1) |
| DOI | 10.11648/j.ajdmkd.20160101.13 |
| Page(s) | 16-28 |
| 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), 2017. Published by Science Publishing Group |
Data Mining Approach, Electric Power Consumption, Forecasting, Gene Expression Programming
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APA Style
S. Hr. Aghay Kaboli, Alireza Fallahpour, Nima Kazemi, Jeyraj Selvaraj, N. A. Rahim. (2017). An Expression-Driven Approach for Long-Term Electric Power Consumption Forecasting. American Journal of Data Mining and Knowledge Discovery, 1(1), 16-28. https://doi.org/10.11648/j.ajdmkd.20160101.13
ACS Style
S. Hr. Aghay Kaboli; Alireza Fallahpour; Nima Kazemi; Jeyraj Selvaraj; N. A. Rahim. An Expression-Driven Approach for Long-Term Electric Power Consumption Forecasting. Am. J. Data Min. Knowl. Discov. 2017, 1(1), 16-28. doi: 10.11648/j.ajdmkd.20160101.13
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Download@article{10.11648/j.ajdmkd.20160101.13,
author = {S. Hr. Aghay Kaboli and Alireza Fallahpour and Nima Kazemi and Jeyraj Selvaraj and N. A. Rahim},
title = {An Expression-Driven Approach for Long-Term Electric Power Consumption Forecasting},
journal = {American Journal of Data Mining and Knowledge Discovery},
volume = {1},
number = {1},
pages = {16-28},
doi = {10.11648/j.ajdmkd.20160101.13},
url = {https://doi.org/10.11648/j.ajdmkd.20160101.13},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.ajdmkd.20160101.13},
abstract = {This study deals with estimation of electricity demand of Iran on the basis of economic criteria using a genetic-based approach called Gene Expression Programming (GEP) as an expression-driven approach. The GEP-based mathematical model is provided based on population, gross domestic product, exports, and imports. The proposed model is derived based on available data obtained from 1992 to 2006. To assess the forecasting accuracy of the model, the electricity demand from 2007 until 2012 are calculated by the GEP-based model and the obtained results are compared with the real data during this period. To show the accuracy of the model, the results obtained by GEP model are compared with the results obtained from Multi-Layer Perceptron (MLP) neural network and Multiple Linear Regression (MLR) as the two conventional methods. In addition, a five-fold cross-validation and future year prediction are used to show the robustness of the model in predicting the electricity demand. Future estimation of Iran's electric energy consumption is then projected up to 2030 according to three different scenarios. Finally, a sensitivity analysis is conducted to identify the most important independent variables affecting electricity demand.},
year = {2017}
}
TY - JOUR T1 - An Expression-Driven Approach for Long-Term Electric Power Consumption Forecasting AU - S. Hr. Aghay Kaboli AU - Alireza Fallahpour AU - Nima Kazemi AU - Jeyraj Selvaraj AU - N. A. Rahim Y1 - 2017/01/13 PY - 2017 N1 - https://doi.org/10.11648/j.ajdmkd.20160101.13 DO - 10.11648/j.ajdmkd.20160101.13 T2 - American Journal of Data Mining and Knowledge Discovery JF - American Journal of Data Mining and Knowledge Discovery JO - American Journal of Data Mining and Knowledge Discovery SP - 16 EP - 28 PB - Science Publishing Group SN - 2578-7837 UR - https://doi.org/10.11648/j.ajdmkd.20160101.13 AB - This study deals with estimation of electricity demand of Iran on the basis of economic criteria using a genetic-based approach called Gene Expression Programming (GEP) as an expression-driven approach. The GEP-based mathematical model is provided based on population, gross domestic product, exports, and imports. The proposed model is derived based on available data obtained from 1992 to 2006. To assess the forecasting accuracy of the model, the electricity demand from 2007 until 2012 are calculated by the GEP-based model and the obtained results are compared with the real data during this period. To show the accuracy of the model, the results obtained by GEP model are compared with the results obtained from Multi-Layer Perceptron (MLP) neural network and Multiple Linear Regression (MLR) as the two conventional methods. In addition, a five-fold cross-validation and future year prediction are used to show the robustness of the model in predicting the electricity demand. Future estimation of Iran's electric energy consumption is then projected up to 2030 according to three different scenarios. Finally, a sensitivity analysis is conducted to identify the most important independent variables affecting electricity demand. VL - 1 IS - 1 ER -
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