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# Modelling electricity sales in Sri Lanka and Colombo city using different statistical methods

#### H.A. Pathberiya,

##### University of Sri Jayewardenepura, Gangodawila, Nugegoda., LK
Department of Statistics and Computer Science, Faculty of Applied Sciences

#### P. Dias

##### University of Sri Jayewardenepura, Gangodawila, Nugegoda., LK
Department of Statistics and Computer Science, Faculty of Applied Sciences

## Abstract

Electricity is an essential form of energy used all over the world and the sales are growing each year owing to population growth, industrialization, etc. Forecasts of electricity sales would be of great importance to electric utilities when deciding on construction and investments. The objective of this study was to develop models suitable for forecasting monthly electricity sales for Sri Lanka and Colombo city. The monthly electricity sales data (in GWh) for Sri Lanka and for Colombo city from 2001 to 2009 were obtained from the Ceylon Electricity Board for this purpose. Three models for forecasting electricity sales were studied using three different approaches, namely, classical decomposition approach, stochastic approach and exponential smoothing approach. The estimated models were compared and the best model for forecasting electricity sales was selected. It is shown that the stochastic model given by ARIMA (0,1,1)(0,1,1)12 generates more accurate forecasts for Colombo city and for Sri Lanka, compared with the other two models. The corresponding mean square deviations are 181.658 and 6.703 for Sri Lanka and Colombo city, respectively.

J.Natn.Sci.Foundation Sri Lanka 2013 41 (1): 41-51

##### Keywords: Classical approach,  exponential smoothing approach,  forecasting,  stochastic approach,  time series
How to Cite: Pathberiya, H.A. & Dias, P., (2013). Modelling electricity sales in Sri Lanka and Colombo city using different statistical methods. Journal of the National Science Foundation of Sri Lanka. 41(1), pp.41–51. DOI: http://doi.org/10.4038/jnsfsr.v41i1.5332
Published on 24 Mar 2013.
Peer Reviewed