Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria

Clement Effiong; Simeon Ozuomba; Udeme John Edet

doi:doi:10.11648/j.sjee.20160406.16

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Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria

Clement Effiong, Simeon Ozuomba, Udeme John Edet

Published in Science Journal of Energy Engineering (Volume 4, Issue 6)

Received: 25 October 2016 Accepted: 10 December 2016 Published: 24 January 2017

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Abstract

This paper presents an approach for long-term estimation and forecasting of electric peak load. A 10-year peak load forecast is performed on Uyo transmission substation in Akwa Ibom State, Nigeria. The peak loads of the past ten years (from 2006 to 2010) are used as input data used to develop the model for forecasting the peak load demand in Uyo metropolis. Particularly, Multiple Linear Regression (MLR) method is used to model the annual peak load. The explanatory variables, namely, temperature, population and gross domestic product are used in the analysis. The peak load model parameters are estimated using only the data of the year 2006 to the year 2012, which accounts for 70% of the entire dataset for training and 30% (that is, 2013 to 2015) of the data are used for cross validation. The results show that with respect to the training dataset the prediction model has Mean Absolute Percentage Error (MAPE) of 0.00613%, Mean Absolute Deviation (MAD) of 0.277743 and Coefficient of Determination (R2) value of 0.99184 which shows that about 99.184% of the peak load are explained by the explanatory variables used in the prediction. Furthermore, with respect to the validation dataset (2013 to 2015) the prediction model has RMSE of 1.038042 and percentage error of less that 2% which shows that the proposed peak-load-demand model can effectively predict the peak load demand for Uyo.

Published in	Science Journal of Energy Engineering (Volume 4, Issue 6)
DOI	10.11648/j.sjee.20160406.16
Page(s)	85-89
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

Keywords

Peak load, Multiple Linear Regression Model, Load Estimation, Load Forecasting, Population, Temperature, Gross Domestic Product (GDP)

References

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[2]	Sarangi, P. K., Singh, N., Chanhan, R. K., and Singh, R., “Short Term Load Forecasting Using Artificial Neural Network: A Comparison with Genetic Algorithm Implementation”, Asian Research Publishing Network (ARPN) Journal of Engineering and Applied Sciences Vol. 4 (9), November 2009.
[3]	Chakrabarti, A., and Halder, S., Power system Analysis. Operation and Control, (2nd edu), PHI learning Private Ltd, New Delhi, 2008.
[4]	Vadhera, S. S., Power System Analysis and Stability, Khana Publishers, NaiSarak, Delhi, 2004.
[5]	Electrical Power Systems Planning”, A. S. Pabla, Macmillan India Ltd., 1988.
[6]	Alexander Bruhns, Gilles Deurveilher and Jean-Sebastien Roy, “A Non-Linear Regression Model for Midterm Load Forecasting and Improvements in Seasonality.” 15th Power Systems Computation Conference (PSCC), Liege, 22-26 August 2005.
[7]	N. Amjady, “Short-Term Hourly Load Forecasting Using Time Series Modeling with Peak Load Estimation Capability”, IEEE Transactions on Power Systems, Vol. 16, No. 3, pp. 498-505, August 2001.
[8]	H. M. Al-Hamadi, S. A. Soliman, “Long Term/Mid-Term Electric Load Forecasting Based on Short-Term Correlation and Annual Growth”, Electric Power Systems Research, Vol. 74, pp. 353-361, 2005.
[9]	M. S. Kandil, S. M. El-Debeiky, N. E. Hasanien, “Overview and Comparison of Long-Term Forecasting Techniques for a Fast Developing Utility: Part I”, Electric Power Systems Research, Vol. 58, pp. 11-17, 2001.
[10]	M. Djukanovic, B. Babic, D. J. Sobajic, Y. H. Pao, “Unsupervised/Supervised Learning Concept for 24-Hour Load Forecasting”, IEE Proceedings-C, Vol. 140, No. 4, pp. 311-318, 1993.
[11]	T. Yalcinoz, U. Eminoglu, “Short Term and Medium Term Power Distribution Load Forecasting by Neural Networks”, Energy Conversion and Management, Vol. 44, pp. 1393-1405, 2005.
[12]	S. Krunic, I. Kircmar, N. Rajakovic, “An Improved Neural Network Application for Short-Term Load Forecasting in Power Systems”, Electric Machines and Power Systems, Vol. 28, pp. 703-721, 2000.
[13]	C. C. Hsu, C. Y. Chen, “Regional Load Forecasting in Taiwan Applications of Artificial Neural Network”, Energy Conversion and Management, Vol. 44, pp. 1941- 1949, 2003.
[14]	S. A. Kalogirou, “Applications of Artificial Neural Networks in Energy Systems a Review”, Energy Conversion and Management, Vol. 40, pp. 1073-1087, 1999.
[15]	http://www.accweather.com/en/ng/uyo/251973/weather-forecast/251973 Accessed on 10t September 2016.

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APA Style

Clement Effiong, Simeon Ozuomba, Udeme John Edet. (2017). Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria. Science Journal of Energy Engineering, 4(6), 85-89. https://doi.org/10.11648/j.sjee.20160406.16

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ACS Style

Clement Effiong; Simeon Ozuomba; Udeme John Edet. Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria. Sci. J. Energy Eng. 2017, 4(6), 85-89. doi: 10.11648/j.sjee.20160406.16

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AMA Style

Clement Effiong, Simeon Ozuomba, Udeme John Edet. Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria. Sci J Energy Eng. 2017;4(6):85-89. doi: 10.11648/j.sjee.20160406.16

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@article{10.11648/j.sjee.20160406.16,
  author = {Clement Effiong and Simeon Ozuomba and Udeme John Edet},
  title = {Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria},
  journal = {Science Journal of Energy Engineering},
  volume = {4},
  number = {6},
  pages = {85-89},
  doi = {10.11648/j.sjee.20160406.16},
  url = {https://doi.org/10.11648/j.sjee.20160406.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjee.20160406.16},
  abstract = {This paper presents an approach for long-term estimation and forecasting of electric peak load. A 10-year peak load forecast is performed on Uyo transmission substation in Akwa Ibom State, Nigeria. The peak loads of the past ten years (from 2006 to 2010) are used as input data used to develop the model for forecasting the peak load demand in Uyo metropolis. Particularly, Multiple Linear Regression (MLR) method is used to model the annual peak load. The explanatory variables, namely, temperature, population and gross domestic product are used in the analysis. The peak load model parameters are estimated using only the data of the year 2006 to the year 2012, which accounts for 70% of the entire dataset for training and 30% (that is, 2013 to 2015) of the data are used for cross validation. The results show that with respect to the training dataset the prediction model has Mean Absolute Percentage Error (MAPE) of 0.00613%, Mean Absolute Deviation (MAD) of 0.277743 and Coefficient of Determination (R2) value of 0.99184 which shows that about 99.184% of the peak load are explained by the explanatory variables used in the prediction. Furthermore, with respect to the validation dataset (2013 to 2015) the prediction model has RMSE of 1.038042 and percentage error of less that 2% which shows that the proposed peak-load-demand model can effectively predict the peak load demand for Uyo.},
 year = {2017}
}

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TY  - JOUR
T1  - Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria
AU  - Clement Effiong
AU  - Simeon Ozuomba
AU  - Udeme John Edet
Y1  - 2017/01/24
PY  - 2017
N1  - https://doi.org/10.11648/j.sjee.20160406.16
DO  - 10.11648/j.sjee.20160406.16
T2  - Science Journal of Energy Engineering
JF  - Science Journal of Energy Engineering
JO  - Science Journal of Energy Engineering
SP  - 85
EP  - 89
PB  - Science Publishing Group
SN  - 2376-8126
UR  - https://doi.org/10.11648/j.sjee.20160406.16
AB  - This paper presents an approach for long-term estimation and forecasting of electric peak load. A 10-year peak load forecast is performed on Uyo transmission substation in Akwa Ibom State, Nigeria. The peak loads of the past ten years (from 2006 to 2010) are used as input data used to develop the model for forecasting the peak load demand in Uyo metropolis. Particularly, Multiple Linear Regression (MLR) method is used to model the annual peak load. The explanatory variables, namely, temperature, population and gross domestic product are used in the analysis. The peak load model parameters are estimated using only the data of the year 2006 to the year 2012, which accounts for 70% of the entire dataset for training and 30% (that is, 2013 to 2015) of the data are used for cross validation. The results show that with respect to the training dataset the prediction model has Mean Absolute Percentage Error (MAPE) of 0.00613%, Mean Absolute Deviation (MAD) of 0.277743 and Coefficient of Determination (R2) value of 0.99184 which shows that about 99.184% of the peak load are explained by the explanatory variables used in the prediction. Furthermore, with respect to the validation dataset (2013 to 2015) the prediction model has RMSE of 1.038042 and percentage error of less that 2% which shows that the proposed peak-load-demand model can effectively predict the peak load demand for Uyo.
VL  - 4
IS  - 6
ER  -

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Author Information

Clement Effiong

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Akwa Ibom, Nigeria
Simeon Ozuomba

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Akwa Ibom, Nigeria
Udeme John Edet

Department of Electrical/Electronic and Computer Engineering, University of Uyo, Akwa Ibom, Nigeria

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APA Style

Clement Effiong, Simeon Ozuomba, Udeme John Edet. (2017). Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria. Science Journal of Energy Engineering, 4(6), 85-89. https://doi.org/10.11648/j.sjee.20160406.16

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ACS Style

Clement Effiong; Simeon Ozuomba; Udeme John Edet. Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria. Sci. J. Energy Eng. 2017, 4(6), 85-89. doi: 10.11648/j.sjee.20160406.16

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AMA Style

Clement Effiong, Simeon Ozuomba, Udeme John Edet. Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria. Sci J Energy Eng. 2017;4(6):85-89. doi: 10.11648/j.sjee.20160406.16

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@article{10.11648/j.sjee.20160406.16,
  author = {Clement Effiong and Simeon Ozuomba and Udeme John Edet},
  title = {Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria},
  journal = {Science Journal of Energy Engineering},
  volume = {4},
  number = {6},
  pages = {85-89},
  doi = {10.11648/j.sjee.20160406.16},
  url = {https://doi.org/10.11648/j.sjee.20160406.16},
  eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.sjee.20160406.16},
  abstract = {This paper presents an approach for long-term estimation and forecasting of electric peak load. A 10-year peak load forecast is performed on Uyo transmission substation in Akwa Ibom State, Nigeria. The peak loads of the past ten years (from 2006 to 2010) are used as input data used to develop the model for forecasting the peak load demand in Uyo metropolis. Particularly, Multiple Linear Regression (MLR) method is used to model the annual peak load. The explanatory variables, namely, temperature, population and gross domestic product are used in the analysis. The peak load model parameters are estimated using only the data of the year 2006 to the year 2012, which accounts for 70% of the entire dataset for training and 30% (that is, 2013 to 2015) of the data are used for cross validation. The results show that with respect to the training dataset the prediction model has Mean Absolute Percentage Error (MAPE) of 0.00613%, Mean Absolute Deviation (MAD) of 0.277743 and Coefficient of Determination (R2) value of 0.99184 which shows that about 99.184% of the peak load are explained by the explanatory variables used in the prediction. Furthermore, with respect to the validation dataset (2013 to 2015) the prediction model has RMSE of 1.038042 and percentage error of less that 2% which shows that the proposed peak-load-demand model can effectively predict the peak load demand for Uyo.},
 year = {2017}
}

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TY  - JOUR
T1  - Long-Term Peak Load Estimate and Forecast: A Case Study of Uyo Transmission Substation, Akwa Ibom State, Nigeria
AU  - Clement Effiong
AU  - Simeon Ozuomba
AU  - Udeme John Edet
Y1  - 2017/01/24
PY  - 2017
N1  - https://doi.org/10.11648/j.sjee.20160406.16
DO  - 10.11648/j.sjee.20160406.16
T2  - Science Journal of Energy Engineering
JF  - Science Journal of Energy Engineering
JO  - Science Journal of Energy Engineering
SP  - 85
EP  - 89
PB  - Science Publishing Group
SN  - 2376-8126
UR  - https://doi.org/10.11648/j.sjee.20160406.16
AB  - This paper presents an approach for long-term estimation and forecasting of electric peak load. A 10-year peak load forecast is performed on Uyo transmission substation in Akwa Ibom State, Nigeria. The peak loads of the past ten years (from 2006 to 2010) are used as input data used to develop the model for forecasting the peak load demand in Uyo metropolis. Particularly, Multiple Linear Regression (MLR) method is used to model the annual peak load. The explanatory variables, namely, temperature, population and gross domestic product are used in the analysis. The peak load model parameters are estimated using only the data of the year 2006 to the year 2012, which accounts for 70% of the entire dataset for training and 30% (that is, 2013 to 2015) of the data are used for cross validation. The results show that with respect to the training dataset the prediction model has Mean Absolute Percentage Error (MAPE) of 0.00613%, Mean Absolute Deviation (MAD) of 0.277743 and Coefficient of Determination (R2) value of 0.99184 which shows that about 99.184% of the peak load are explained by the explanatory variables used in the prediction. Furthermore, with respect to the validation dataset (2013 to 2015) the prediction model has RMSE of 1.038042 and percentage error of less that 2% which shows that the proposed peak-load-demand model can effectively predict the peak load demand for Uyo.
VL  - 4
IS  - 6
ER  -

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