We apologize for a recent technical issue with our email system, which temporarily affected account activations. Accounts have now been activated. Authors may proceed with paper submissions. PhDFocusTM
CFP last date
20 November 2024
Call for Paper
December Edition
IJCA solicits high quality original research papers for the upcoming December edition of the journal. The last date of research paper submission is 20 November 2024

Submit your paper
Know more
Reseach Article

Modeling Electricity Consumption using Modified Newton’s Method

by P. Ozoh, S. Abd-rahman, J. Labadin, M. Apperley
International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 86 - Number 13
Year of Publication: 2014
Authors: P. Ozoh, S. Abd-rahman, J. Labadin, M. Apperley
10.5120/15046-3414

P. Ozoh, S. Abd-rahman, J. Labadin, M. Apperley . Modeling Electricity Consumption using Modified Newton’s Method. International Journal of Computer Applications. 86, 13 ( January 2014), 27-31. DOI=10.5120/15046-3414

@article{ 10.5120/15046-3414,
author = { P. Ozoh, S. Abd-rahman, J. Labadin, M. Apperley },
title = { Modeling Electricity Consumption using Modified Newton’s Method },
journal = { International Journal of Computer Applications },
issue_date = { January 2014 },
volume = { 86 },
number = { 13 },
month = { January },
year = { 2014 },
issn = { 0975-8887 },
pages = { 27-31 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume86/number13/15046-3414/ },
doi = { 10.5120/15046-3414 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T22:04:07.964835+05:30
%A P. Ozoh
%A S. Abd-rahman
%A J. Labadin
%A M. Apperley
%T Modeling Electricity Consumption using Modified Newton’s Method
%J International Journal of Computer Applications
%@ 0975-8887
%V 86
%N 13
%P 27-31
%D 2014
%I Foundation of Computer Science (FCS), NY, USA
Abstract

In this paper we present modified Newton's model (MNM) to model electricity consumption data. A previous method to model electricity consumption data was done using forecasting technique (FT) and artificial neural networks (ANN). A drawback to previous techniques is that computations give less reliable results when compared to MNM. A comparative analysis is carried out for FT, ANN and MNM to investigate which of these methods is the most reliable technique. The results indicate that MNM model reduced mean absolute percentage error (MAPE) to 0. 93%, while those of FT and ANN were 3. 01% and 3. 11%, respectively. Based on these error measures, the study shows that the three methods are highly accurate modeling techniques, but MNM was found to be the best technique when mining information. Experimental results indicate that MNM is the most accurate when compared to FT and ANN and thus has the best competitive performance level.

References
  1. L. Sughnathi and A. A. Samuel, "Energy models for demand forecasting - A Review," Renew. Sustain. Energy Rev. , vol. 16, pp. 1223–1240, 2012.
  2. C. D. Lewis, Industrial and Business Forecasting Methods: A practical guide to exponential smoothing and curve fitting, Butterworth Scientific, London, 1982.
  3. J. C. Lam, K. K. W. Wan, D. Liu, and C. L. Tsang, "Multiple Regression Models for Energy Use in Air-conditioned Office Buildings in Different Climates," Energy Convers. Manag. , pp. 2692–2697, 2010.
  4. M. Akole, M. Bongulwar, and B. Tyagi, "Predictive model of load and price for restructured power system using neural network," Int. Conf. Energy, Autom. Signal, pp. 1–6, 2011.
  5. J. Lei, H. Ning, and L. Yao, "A new method of load-shedding control on centrifugal water chiller sequencing," 2009 4th IEEE Conf. Ind. Electron. Appl. , pp. 3204–3209, May 2009.
  6. P. Hsiao-Tien and T. Chung-Ming, "Modeling and forecasting the CO 2 emissions, energy consumption, and economic growth in Brazil," 5th Conf. Sustain. Dev. Energy, Water Environ. Syst. , vol. 36, no. 5, pp. 2315–3618, 2011.
  7. P. Monigatti, M. Apperley, and B. Rogers, "Power and Energy Visualization for the Micro-management of Household Electricity Consumption," Prococeedings AVI 2010, Rome, pp. 325–328, 2010.
  8. Q. Cui, B. Luo, X. Huang, A. Dowhuszko, and J. Jiang, "Closed-form solution for minimizing power consumption in coordinated transmissions," EURASIP J. Wirel. Commun. Netw. , vol. 2012, no. 1, p. 122, 2012.
  9. Q. Wang, X. Wang, and F. Xia, "Integration of Grey Model and Multiple Regression Model to Predict Energy Consumption," IEEE Proc. , pp. 194–197, 2009.
  10. O. Kramer, B. Satzger, and J. Lassig, "Power Prediction in Smart Grids with Evolutionary Local Kernel Regression," HAIS Part 1 LNAI 6076, pp. 262–269, 2010.
  11. H. Chen, C. A. Canizares, and A. Singh, "ANN-based short-term load forecasting in electricity markets," 2001 IEEE Power Eng. Soc. Winter Meet. Conf. Proc. (Cat. No. 01CH37194), vol. 2, no. 1, pp. 411–415, 2001.
Index Terms

Computer Science
Information Sciences

Keywords

Efficiency modified newton's method forecasting technique artificial neural networks reliability