Electricity demand for Sri lanka: A time series analysis

This study estimates electricity demand functions for Sri Lanka using six econometric techniques. It shows that the preferred specifications differ somewhat and there is a wide range in the long-run price and income elasticities with the estimated long-run income elasticity ranging from 1.0 to 2.0 and the long run price elasticity from 0 to –0.06. There is also a wide range of estimates of the speed with which consumers would adjust to any disequilibrium, although the estimated impact income elasticities tended to be more in agreement ranging from 1.8 to 2.0. Furthermore, the estimated effect of the underlying energy demand trend varies between the different techniques; ranging from being positive to zero to predominantly negative. Despite these differences the forecasts generated from the six models up until 2025 do not differ significantly. Thus on one hand it is encouraging that the Sri Lanka electricity authorities can have some faith in econometrically estimated models used for forecasting. However, by the end of the forecast period in 2025 there is a variation of around 452MW in the base forecast peak demand; which, in relative terms for a small electricity generation system like Sri Lanka’s, represents a considerable difference

Estimating the evolution of elasticities of natural gas demand: the case of Istanbul, Turkey

Much of the existing literature on demand for natural gas assumes constant and single-value elasticities, overlooking the possibility of dynamic responses to the changing conditions. We aim to fill this gap by providing individual time series of short-run elasticity estimates based on maximum entropy resampling in a fixed-width rolling window framework. This approach does not only enable taking the variability of the elasticities into account, but also helps obtain more efficient and robust results in small samples in comparison with conventional inferences based on asymptotic distribution theory. To illustrate the methodology, we employ monthly time-series data between 2004 and 2012 and analyze the dynamics of residential natural gas demand in Istanbul, the largest metropolitan area in Turkey. Our findings reveal that the elasticities of the demand model do not remain constant and they are sensitive to the economic situation as well as weather fluctuations

Bootstrap Inference of Level Relationships in the Presence of Serially Correlated Errors: A Large Scale Simulation Study and an Application in Energy Demand

By undertaking a large scale simulation study, we demonstrate that the maximum entropy bootstrap (meboot) data generation process can provide accurate and narrow parameter confidence intervals in models with combinations of stationary and nonstationary variables, under both low and high degrees of autocorrelation. The relatively small sample sizes in which meboot performs particularly well make it a useful tool for rolling window estimation. As a case study, we analyze the evolution of the price and income elasticities of import demand for crude oil in Turkey by using quarterly data between 1996-2011. Our approach can be employed to tackle a wide range of macroeconometric estimation problems where small sample sizes are a common issue

An integrated fuzzy regression–analysis of variance algorithm for improvement of electricity consumption estimation in uncertain environments

This study presents an integrated fuzzy regression analysis of variance (ANOVA) algorithm to estimate andpredict electricity consumption in uncertain environment. The proposed algorithm is composed of 16 fuzzy regression models. This is because there is no clear cut as to which of the recent fuzzy regression model is suitable for a given set of actual data with respect to electricity consumption. Furthermore, it is difficult to model uncertain behavior of electricity consumption with conventional time series and proper fuzzy regression could be an ideal substitute for such cases. The algorithm selects the best model by mean absolute percentage error (MAPE), index of confidence (IC), distance measure, and ANOVA for electricity estimation and prediction. Monthly electricity consumption of Iran from 1992 to 2004 is considered to show the applicability and superiority of the proposed algorithm. The unique features of this study are threefold. The proposed algorithm selects the best fuzzy regression model for a given set of uncertain data by standard andproven methods. The selection process is based on MAPE, IC, distance to ideal point, and ANOVA. In contrast to previous studies, this study presents an integrated approach because it considers the most important fuzzy regression approaches, MAPE, IC, distance measure, and ANOVA for selection of the preferred model for the given data. Moreover, it always guarantees the preferred solution through its integrated mechanism