Google scholar arxiv informatics ads IJAIS publications are indexed with Google Scholar, NASA ADS, Informatics et. al.

Call for Paper


July Edition 2023

International Journal of Applied Information Systems solicits high quality original research papers for the July 2023 Edition of the journal. The last date of research paper submission is June 15, 2023.

A Procedure for the Analysis of Multivariate Factors Affecting Electricity Consumption

P. Ozoh, S. Abd-Rahman Published in Information Sciences

International Journal of Applied Information Systems
Year of Publication: 2017
Publisher: Foundation of Computer Science (FCS), NY, USA
Authors:P. Ozoh, S. Abd-Rahman
Download full text
  1. P Ozoh and S Abd-Rahman. A Procedure for the Analysis of Multivariate Factors Affecting Electricity Consumption. International Journal of Applied Information Systems 12(9):8-12, December 2017. URL, DOI BibTeX

    	author = "P. Ozoh and S. Abd-Rahman",
    	title = "A Procedure for the Analysis of Multivariate Factors Affecting Electricity Consumption",
    	journal = "International Journal of Applied Information Systems",
    	issue_date = "December 2017",
    	volume = 12,
    	number = 9,
    	month = "Dec",
    	year = 2017,
    	issn = "2249-0868",
    	pages = "8-12",
    	url = "",
    	doi = "10.5120/ijais2017451726",
    	publisher = "Foundation of Computer Science (FCS), NY, USA",
    	address = "New York, USA"


This research explores the dynamic relationship between temperature and level of building occupancy; and their effect on electricity consumption of electric appliances. It develops a model for electricity consumption based on these variables. It is important that reliable electricity consumption models are employed in finding solution to energy needs, otherwise inappropriate models may result in poor estimates for decision making. In this research, models for the daily electricity consumption for a local university in Malaysia was developed based on extraneous factors, such as temperature and level of building occupancy . As a result of developing such models, social and economic welfare will be improved.


  1. Taspinar, F., Çelebi, N, and Tutkun, N. 2013. .Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods. Energy Building, vol. 56, pp. 23–31.
  2. International Energy Agency. 2013. Key World Energy Statistics.
  3. Tripathi, S. 2014. Day ahead hourly load forecast of PJM electricity market and ISO New England market by using artificial neural network. Innovative Smart Grid Technology Conference, pp. 1–5.
  4. Yedra, R, Diaz, F, and Nieto, M. 2014. A Neural Network Model for Energy Consumption Prediction of CIESOL Bioclamitic Building. Advanced Intelligent Systems.
  5. Marvuglia, A. 2012. Forecasting Using Recurrent Artificial Neural Networks to Consumption, Household Electricity. Energy Procedia, vol. 14, p. 1.
  6. Ozoh, P, Abd-Rahman, S, Labadin, J, and Apperley, M. 2014. A Comparative Analysis of Techniques for Forecasting Electricity Consumption. International Journal of Computer Applications, vol. 88, no. 15, pp. 8–12.
  7. Akole, M and Bongulwar, M. 2011. Predictive model of load and price for restructured power system using neural network. International Conference on Energy, Automata Signal Processing, pp. 1–6.
  8. Kandananond. K. 2011. Forecasting Electricity Demand in Thailand with an Artificial Neural Network Approach. Energies, vol. 4, no. 12, pp. 1246–1257.
  9. Goh, B. 1998. Forecasting residential construction demand in Singapore: a comparative study of the accuracy of time series, regression and artificial neural network techniques,” Engineering Construction and Architectural Management., vol. 5, no. 3, pp. 261–275.
  10. Bacher, P, Madsen, H, Nielsen, A, and Perers, B. 2013. Short-term heat load forecasting for single family houses. Industrial Electronic Society, pp. 5741 – 5746.
  11. Chia, T, Chow, P, and Chizeck, H. Recursive parameter identification of constrained systems: an application to electrically stimulated muscle. 1991. IEEE Transaction Biomedical Engineering., vol. 38, no. 5, pp. 429–42.
  12. Lam, T, Wan, K, and Liu, C. 2010. Multiple Regression Models for Energy Use in Air-conditioned Office Buildings in Different Climates. Energy Conversation. Management, pp. 2692–2697.
  13. Bianco, V, Manca,, O, and Nardini, S. 2009., Electricity consumption forecasting in Italy using linear regression models. Energy, vol. 34, no. 9, pp. 1413–1421.
  14. Braun, M, Altan, H, and Beck, S. 2014. Using regression analysis to predict the future energy consumption of a supermarket in the UK. Applied Energy, vol. 130, pp. 305–313.
  15. Zhang, G, Areekul, P, Member, S, Senjyu, T, and H. Toyama. 2010. A Hybrid ARIMA and Neural Network Model for Short-Term Price Forecasting in Deregulated Market. IEEE Transactional Power System, vol. 25, no. 1, pp. 524–530.
  16. Azadeh, A, Asadzadeh, S, Saberi, M, Nadimi, V, Tajvidi, A, and M. Sheikalishahi. 2011. A Neuro-fuzzy-stochastic frontier analysis approach for long-term natural gas consumption forecasting and behavior analysis: The cases of Bahrain, Saudi Arabia, Syria, and UAE. Applied Energy, vol. 88, no. 11, pp. 3850–3859.
  17. Chogumaira, E. 2011. Short-Term Electricity Price Forecasting Using a Combination of Neural Networks and Fuzzy Inference. Energy Power Engineering, vol. 03, no. 01, pp. 9–16.
  18. Oldewurtela, F, Parisiob, A, Jonesc, C, Gyalistrasa, D, Gwerderd, M, Stauche, V, Lehmannf, B, and M. Morari. 2012. Use of model predictive control and weather forecasts for energy efficient building climate control. Energy Building, vol. 45, pp. 15–27.
  19. Wallace, M, McBride, R, Aumi, S, Mhaskar, P, House, J, and Salsbury, T. 2012. Energy efficient model predictive building temperature control. Chemical Engineering Society, vol. 69, no. 1, pp. 45–58.
  20. Diebel, J. 2013. “WeatherSpark,”
  21. Damak, S. 2011. Applications of two identification methods for an electric distribution system. Journal of Automata System Engineering, vol. 4, no. 5–4, pp. 176–184.
  22. Fox, J. 2002. Structural Equation Models.
  23. Zhang, G, Patuwo, B, and Hu, Y. 1998. Forecasting with artificial neural networks?: The state of the art. vol. 14, pp. 35–62.
  24. Erdogdu, E. 2009. Electricity Demand Analysis Using Cointegration and ARIMA Modeling: A case study of Turkey. Turkey Energy Policy, vol. 35, no. 2.
  25. Ozoh, P, Abd-Rahman, S, Labadin, J, and. Apperley, M. 2014. Modeling Electricity Consumption using Modified Newton’s Method. International. Journal. Computer. Applications, vol. 86, no. 13, pp. 27–31.


Dynamic relationship, modeling, reliability, energy needs, decision making