Forecasting Model of the Electricity Consumption: Preliminary Study using Statistical Data Analysis and Artificial Neural Network (SDA-ANN) Framework
About this article
DOI:
https://doi.org/10.14419/ijet.v7i4.22.27864Keywords:
Artificial Neural Network (ANN), electricity consumption, prediction model, statistical data analysis (SDA), forecastAbstract
There is a growing trend in utilizing forecast model in predicting energy usage in order to pursue sustainable practice. Among the benefit of the prediction model includes energy efficient usage and basic reduction in operating cost. The objective of this paper to employ
Artificial Neural Network (ANN) to be used as the preliminary forecast model for electricity consumption trend prediction. The proposed ANN model will also include on pre-processing of the input variable through Statistical Data Analysis method to improve on the model. The model is then subjected to case study of electricity consumption in Universiti Teknologi MARA Pasir Gudang (UiTMPG), where the environmental temperature, number of student and staff noted as attendance data is designated as the input variables. Actual monthly billing data and UiTM attendance data is used as the testing data for the ANN, and the forecast model successfully generates the
prediction equation. Results from the forecast model show the recorded Mean Relative Error (MRE) is within the acceptable value of 3.0254%.
References
F. Kaytez, M. C. Taplamacioglu, E. Cam, and F. Hardalac,
"Forecasting electricity consumption: A comparison of
regression analysis, neural networks and least squares support vec-tor machines," International Journal of Electrical Power & Energy Systems, vol. 67, pp. 431-438.
Y. T. Chae, R. Horesh, Y. Hwang, and Y. M. Lee, "Artificial neu-ral network model for forecasting sub-hourly electricity
usage in commercial buildings," Energy and Buildings, vol. 111, pp. 184-194.
View more references (19)
L. Suganthi and A. A. Samuel, "Energy models for demand fore-casting—A review," Renewable and sustainable energy
reviews, vol. 16, pp. 1223-1240, 2012.
H.-Z. Li, S. Guo, C.-J. Li, and J.-Q. Sun, "A hybrid annual power load forecasting model based on generalized regression neural
network with fruit fly optimization algorithm," Knowledge-Based Systems, vol. 37, pp. 378-387.
G. Tamizharasi, S. Kathiresan, and K. S. Sreenivasan, "Energy forecasting using artificial neural networks," International Journal of Advanced Research in Electrical, Electronics and
Instrumentation Engineering, vol. 3.
K. Amber, R. Ahmad, M. Aslam, A. Kousar, M. Usman, and M. Khan, "Intelligent Techniques for Forecasting Electricity
Consumption of Buildings," Energy, 2018.
S. S. Baboo and I. K. Shereef, "An efficient weather forecasting system using artificial neural network," International journal of
environmental science and development, vol. 1, p. 321, 2010.
P. P. Balestrassi, E. Popova, A. d. Paiva, and J. M. Lima,
"Design of experiments on neural network's training for
nonlinear time series forecasting," Neurocomputing, vol. 72, pp. 1160-1178, 2009.
A. Azadeh, S. Ghaderi, and S. Sohrabkhani, "A simulated-based neural network algorithm for forecasting electrical
energy consumption in Iran," Energy Policy, vol. 36, pp. 2637-2644, 2008.
M. Sheikhan, M. Bejani, and D. Gharavian, "Modular neural-SVM scheme for speech emotion recognition using ANOVA feature se-lection method," Neural Computing and Applications, vol. 23, pp. 215-227, 2013.
F. Faul, E. Erdfelder, A. Buchner, and A.-G. Lang, "Statistical power analyses using G* Power 3.1: Tests for correlation and re-gression analyses," Behavior research methods, vol. 41, pp. 1149-1160, 2009.
M. T. Sattari, K. Yurekli, and M. Pal, "Performance evaluation of artificial neural network approaches in forecasting reservoir in-flow," Applied Mathematical Modelling, vol. 36, pp. 2649-2657, 2012.
N. Q. Hung, M. S. Babel, S. Weesakul, and N. K. Tripathi, "An artificial neural network model for rainfall forecasting in Bangkok, Thailand," Hydrology and Earth System Sciences, vol. 13, pp. 1413-1425, 2009.