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Sustainability in Oman: Energy Consumption Forecasting using R


  • Department of Electrical and Computer Engineering, Caledonian College of Engineering, Al Hail, Muscat, Oman


Objective: Smart city projects are still in their initial research stages in Oman. This paper aims to prove the effectiveness of smart cities by using Data Mining Techniques (DMT) to predict energy consumption in Oman. Methods: Data collected from thirteen residential and eight industrial meters are used for electricity consumption forecast. Detailed data analysis is carried out using K-means clustering and time-series forecasting in R. Energy consumption data is modeled using average, naive, seasonal naive, Seasonal decomposition of Time Series by Loess (STL) +Random Walk with Drift (RWD), Exponential smoothing state space model with Box-Cox transformation, ARMA errors, Trend and Seasonal component (TBATS) and Autoregressive Integrated Moving Average (ARIMA) models. Findings: Even though the dataset isn’t characterized by seasons or trends, Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE) error measures suggest that electricity consumption for residential sector is more accurately forecasted using TBATS model. Energy consumption for small, medium and large scale industries, on the other hand are more accurately predicted by TBATS, Average and STL + RWD models respectively. Applications: The obtained results confirm the efficiency in forecasting energy consumption in Oman using time series models in order to initiate smart city implementation.


Data Mining, Energy Consumption, Smart City, Clustering, Time-Series Forecasting, R.

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  • Costa C, Santos MY. Improving cities sustainability through the use of data mining in a context of big city data. Proceedings of the 2015 International Conference of Data Mining and Knowledge Engineering, London. 2015, pp. 320-25. PMid:26056886
  • Guzey O. Data Mining in Constrained Random Verification. PhD Dissertation. Santa Barbara: University of California, Department of Electrical and Computer Engineering: Santa Barbara, 2008.
  • Figueiredo V, Rodrigues F, Vale Z, Gouveia JB. An Electric Energy Consumer Characterization Framework based on Data Mining Techniques. IEEE Transactions on Power Systems. 2005 May, 20(2), pp. 596-602. Crossref
  • Sultanate of Oman Renewables Readiness Assessment. Crossref. Date accessed: 16/04/2016.
  • Oman Energy Situation. Crossref. Date accessed: 21/03/2016.
  • These are the most sustainable cities in the world. Crossref. Date accessed: 8/06/2016.
  • Residential Energy Use In Oman:A Scoping Study. Crossref. Date accessed:12/12/2015.
  • Zurigat YH. Analysis of Typical Meteorological Year for Seeb, Muscat, Oman. International Journal of Low Carbon Technologies. 2007 Apr; 2(4):323-38. Crossref
  • Smart Data & Well-being. 2015. Available from: Crossref
  • What a smart home can do. 2016. Available from: Crossref
  • Energy efficiency can halve gas consumption in Oman. 2016. Available from: Crossref
  • Four main languages for Analytics. Data Mining, Data Science. 2016. Available from: 2014/08/four-main-languages-analytics-data-mining-datascience.html
  • R Documentation. 2016. Available from: Crossref
  • Forecasting: Principles and practice. 2016. Available from: Crossref
  • Sajana T, Rani CMS, Narayana KV. A survey on clustering techniques for big data mining. Indian Journal of Science and Technology. 2016 Jan; 9(3):1-4. Crossref


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