Modelling sector-wise demand for electricity in Sri Lanka using a multivariate regression approach

Abstract

A b s t r a c t : The demand for electricity in Sri Lanlca depends mainly on the activities ol'clomestic, industrial and comnlercial sectors ancl t h e t h r e e conlponents a r e highly correlatecl. Although such correlation does n o t affect u n i v a r i a t e e s t i m a t i o n procedures, it may lead to incorrect inferences of influerltial factors on the demand for electricity. As a r e s u l t , s e p a r a t e u n i v a r i a t e a p p r o a c l~e s for each sector m a y not bc a n a c c u r ;~t e niethocl of' identifying s u c h factors. Therefore, this stucly alms to identify sucll facto1.s using multivariate regression whicb consjders the correlation among different sectors (or dependent variables) ancl estimates il multivariate demand model for the purpose offorecasting. The overall sign iricance of t h e fitted demand model a n d t h e significant influential factors a r e assessed by m u l t i v a r i a t e t e s t s s u c h a s B a r t l e t t ' s u s i n g t h e s t a t i s t i c a l package SAS. Theoretically, d e m a n d i s a function of i t s own price, t h e income level ,or consumers, and the price of substitutes. Gross Domestic Product (GDP) a t constant (1960) factor prices is used as a proxy for income level of consumers a n d kerosene i s tillten a s a close substitute Ibl. elcctriclty. The analysis uses quarterly data Tot two periocls 1970-1977 ant1 1978-1994 to a s s e s s t h e effect of t h e liberalized economy int~~ocluccd in late 1977. During the period aftcr 1977, the effect ol'thc income levcl h a s i~lcreasecl s u b s t a~l t i a l l y clue to t h e liberalized economy. T h e substitution between electricity and lierosene i s marginal ill the post-lil)ol~alizecl periocl, a s electricity is more efficient antl-convenient t l~a n Iterosene. Jlue to , such dit'ferenccs between t h e two pcriohs. t h e de~nancl for electricity lni>y be explained better by tnto models rather than a single lnoclel estimated for t l~e entire period. The multivariate demand model based on the post-li.beralized period is fbuncl to aclequately forecast the clemand for electricity.

Evaluating the effectiveness of parametric and nonparametric energy consumption forecasts for a developing country

“…Accurate energy consumption forecasts may translate into lucrative policy decisions and efficient resource allocations which can ensure that events such as the 1996 power crisis experienced in Sri Lanka [2,3] would remain a matter restricted solely to the past. Morimoto and Hope [4] proved that electricity supply has a statistically significant impact on Sri Lanka's economic growth.…”

“…One noteworthy publication is by Ruwanthi and Wickremasinghe [2], in which they used regression analysis to forecast electricity consumption in the 1980's and 1990's. More information on Sri Lanka's landscape in terms of economical, political and energy aspects can be found in [9].…”

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

“…The demand for electricity depends on factors such as population growth, economic activities and electricity price. A study by Ruwanthi and Wickremasinghe (1999) has shown that forecasts of electricity demand in Sri Lanka in any given period depends on the price of electricity and the GDP pertaining to that period. The demand for electricity is increasing over time, and an energy crisis is one of the most critical problems that may be faced by future generations.…”

“…Forecasts on electricity sales will determine the amount of electric power that should be generated, transmitted and supplied in the immediate future. Some past studies (Ruwanthi & Wickremasinghe, 1999;Zuhaimy & Khairil, 2005;Kareem & Majeed, 2006) have investigated various techniques to obtain accurate forecasts for electricity sales. Forecasts are crucial for electricity utilities to establish sustainable power development plans in order to achieve their objective of providing a good service to meet consumers' demand.…”