Economic variables and electricity consumption in Northern Cyprus

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1 Energy 26 (2001) Economic variables and electricity consumption in Northern Cyprus F. Egelioglu a,*, A.A. Mohamad a, H. Guven b a Department of Mechanical Engineering, Eastern Mediterranean University, Magosa, Mersin 10, Turkey b Bahcesehir University, Bahcesehir, Istanbul, Turkey Received 1 September 1999 Abstract The influence of economic variables on the annual electricity consumption in N. Cyprus for the period of has been investigated. Utilising historical energy consumption, historical economic databases and multiple regression analyses, it was found that the number of customers, the price of electricity and the number of tourists correlate with the annual electricity consumption. The annual energy consumption was strongly related to the number of the customers, with adjusted R 2 equal to and if the price of electricity and the number of tourists were included in the model. The results indicate that the model using the number of customers, the number of tourists and the electricity prices as regressors has very strong predictive ability and can be used to forecast future annual electricity consumption Elsevier Science Ltd. All rights reserved. 1. Introduction Modelling electrical demand and energy consumption is usually based on historical consumption and the relationship of this consumption to other relevant variables, such as; economic, demographic, climatic, etc. A large number of studies have been published [1 8] on electrical demand and energy consumption models. Yan [1] presented residential electricity consumption models using climatic variables for Hong Kong. Rajan [3] expressed energy consumption patterns for Delhi as functions of weather and population. Sheinbaum [5] showed that Mexican residential energy demand is inelastic to energy price. Variables affecting demand and energy consumption may vary from one region to another. A model developed for one region may not be appropriate for another region. Electrical demand and energy models are required for a variety of utility * Corresponding author. Fax: address: fuat.egelioglu@emu.edu.tr (F. Egelioglu) /01/$ - see front matter 2001 Elsevier Science Ltd. All rights reserved. PII: S (01)

2 356 F. Egelioglu et al. / Energy 26 (2001) activities. Therefore, models should be developed for different regions for efficient planning and organisation. Cyprus has no oil or gas reserves and is totally dependent on imported energy, mainly in the form of oil and petroleum products. S. Cyprus supplied about 90% of the electrical energy consumed in N. Cyprus between the years 1974 and The supply and demand of electrical energy in N. Cyprus is continuously tracked by the local state run utility company, KIB-TEK. Low priced electricity was indiscriminately used for space heating, water heating and pumping irrigation water out of underground wells. In 1995, N. Cyprus generated 90% of her own electricity and the price (or rates) of electricity was increased from US$0.02/kWh to US$0.062/kWh [9]. The government controls the price of electricity and today the electricity rates (approximately US$0.06/kWh) are still below the world s standard. The annual growth rate in electricity consumption between 1988 and 1997 was 6% [9,10]. The primary reason behind this rapid growth is the booming construction industry; the low electricity rates are also a factor. Currently KIB-TEK is simply following the consumer demand. The main goal of KIB-TEK is to meet customer load without any ability to control electricity consumption. Rising costs, high rate load growth and low electricity rates are some factors adversely affecting electric utilities of developing countries. KIB-TEK needs future estimations of power requirements for effective and efficient planning. For long term planning (i.e., years) utilities need to develop electrical demand and consumption models, which directly account for the impact of economic variables upon energy consumption [11]. The main objective of this study is to provide annual electricity consumption models using economic and weather variables as regressors. 2. Methods 2.1. Sources of data For the period of , the values of annual electricity consumption (Y), electricity rates (P) and the number of customers (C) were obtained from annually published KIB-TEK reports [9,10]. Per capita income (I), population (Pop) and the number of tourists (T) were obtained from the statistical data published by the Planning and Organisation Department [12]. Hourly temperatures used to calculate the heating degree days (HDD) were obtained from the Meteorological Office [13] Description of the modelling technique To investigate the influence of the economic and climatic variables on annual electricity consumption, the multiple regression method is used. The model equation is Y b 0 b 1 X 1 b 2 X 2 b 3 X 3 b 4 X 4 b 5 X 5 b 6 X 6 e (1) where Y represents the annual electrical energy consumption, b values are regression coefficients and e is the unknown disturbance term (i.e., error or residuals). Measurement error for the dependent and independent variables, the random nature of human responses and effect of omitted

3 F. Egelioglu et al. / Energy 26 (2001) variables are the main sources of random disturbance [11]. The X values represent the six independent variables that may be used as predictors of Y (i.e., P, C, I, Pop, T and HDD). Two types of independent variables should be considered: internal and external variables. Internal variables (i.e., controllable variables) are factors that are influenced by the utility s internal environment, such as price of electricity, number of customers and incentive program levels. External variables (i.e., uncontrollable variables) include factors that are affected by the utility s external environment, such as price of competing products, population, per capita income, tourism, new businesses, unemployment rate, retail sales, heating/cooling degree days, etc. The variables that may affect the electricity consumption should be selected and the final model will be reached after eliminating the unimportant variables Data analysis KIB-TEK has been charging different energy rates only to different customer classes (i.e., residential, commercial, agricultural and industrial). In this study, models were developed by combining all of the customer classes. A weighted average of the individual customer class rates were found and used in the analyses. The equation for weighted average rate is Average rate N i 1 GWh i Rate i N GWh i i 1 where GWh i is the energy consumption for the ith rate class, and Rate i is the rate charged to the ith rate class. Fig. 1 shows a time plot of the average rate of electricity. Until 1995, energy supplied by S. Cyprus was not charged to N. Cyprus by mutual agreement. Thus, the energy rates in N. Cyprus were very low. In 1995, KIB-TEK started to generate most of the energy consumed in N. Cyprus and increased the energy rates to cover the generation cost. The unbudgeted electricity consumption (i.e., electricity consumption that is not directly charged to the users, such as energy consumption in public offices) is not considered in this study. (2) Fig. 1. Weighted average electricity rate.

4 358 F. Egelioglu et al. / Energy 26 (2001) Fig. 2. Electricity consumption in N. Cyprus. Fig. 2 represents the annual electricity consumption. There has been a continuous increase in electricity consumption, with the possible exceptions of 1994 and After investigating the data for 1994 and 1995, it was concluded that there was a reduction in the power supplied by S. Cyprus in 1994 and in 1995 the rates were increased by 220%, causing consumers to use electricity more conservatively. Fig. 3 shows a time plot of the number of customers and population. The number of customers and population increase almost linearly and parallel to each other. To visualise the relationships between dependent and independent variables, scatter plots of independent variables versus electricity consumption are presented in Fig. 4. There is a strong linear relationship between population and electricity consumption, but population was removed from further consideration to prevent multicollinearity problems as the number of customers and population are highly correlated (i.e., the correlation between them is 0.999). The data on dependent and independent variables are used to estimate the regression coefficients in Eq. (1). Graphical method, adjusted R 2 method, F-test and the scatter plot of residuals were used to evaluate the appropriateness of the models. Residual plots against variables, which were not included in the model, were also examined. The time series method is used to develop linear trend models for further validation of the developed regression models. The linear trend model equation is Y t a 1 a 2 t (3) where t is the time, Y t is the energy consumption at that time, a 1 and a 2 are unknown parameters. Fig. 3. Time plot of number of customers and population.

5 F. Egelioglu et al. / Energy 26 (2001) Fig. 4. Scatter plots of electricity consumption vs independent variables. Parameter a 1 is the starting point for the model and a 2 is the annual constant rate of change for energy consumption. Further details of the linear trend models can be found in [11]. 3. Results and discussion For the study, all possible regression models (i.e., a total number of 2 5 ) were calculated. Five regression models and a time-series model were provided. The results of the regression and time series analyses are presented in Table 1. R 2 values are maximised in model 1, model 2, model 3, model 4 and model 5 by using one, two, three, four and five independent variables respectively. In the first model, the electricity consumption is assumed to be explained only by the number of customers and R 2 value was calculated to be Although the R 2 value is high, it is unreasonable to assume that the number of customers is the only variable that influences electrical energy consumption. Every additional independent variable to models will result in an increase in R 2. Model 5, which contains all independent variables, gives the largest value of R 2. Clearly not all these explanatory variables should be included. Therefore, maximising the value of R 2 (i.e., including all possible explanatory variables in the model) is not an appropriate method for finding the best model. To find the best model an adjusted R 2 was used. Model 3 has the highest adjusted R 2 value. So the final regression model is as follows: Y C 0.663P 0.158T (4) The F-ratio for model 3 is , is much greater than the critical value of F=4.76 for degrees of freedom (3,6) at the 5% level of significance. This indicates that the model is highly significant. Fig. 5 plots the historical predicted values from the models vs the actual values of electricity consumption for the period of study. The historical predicted values from model 3 and the actual

6 360 F. Egelioglu et al. / Energy 26 (2001) Table 1 Energy consumption model summary Model Coefficients R 2 Adjusted R 2 F-Ratio 1 Constant C Constant C P Constant C P T Constant C P T I Constant C P T I HDD a a Fig. 5. Actual and predicted electricity consumption in N. Cyprus.

7 F. Egelioglu et al. / Energy 26 (2001) values lie much closer to each other compared to other models. The figure indicates that model 3 is the best-developed model. The lower right panel shows the historical predicted values from model 6 vs the actual values; this panel also shows that model 3 is an appropriate model. Fig. 6 shows four plots of the residuals after fitting model 3. The lower right panel shows the residuals plotted against the fitted values. The other plots show the residuals plotted against the three explanatory variables. Randomly scattered residuals indicate that the regression can be considered appropriate. 4. Conclusions and future work For the present study, five regression models have been tested: it is suggested that model 3 can be used to estimate the future electricity consumption by forecasting values for the independent variables (i.e., price of electricity, number of customers and tourists) used in the model. Various methods were used to evaluate the appropriateness of the models. Each method gives different information. Relying on only one of the methods may cause erroneous conclusion. All possible regression models have been calculated and the best model has been chosen among them. However, this becomes impractical when the number of independent variables is too large to allow all possible regression models to be computed. The stepwise regression methods can be used when there is a large number of combinations of variables. Neither the stepwise forward nor the stepwise backward method is guaranteed to produce the optimal pair of independent variables, or triple of independent variables, and so on [14]. We often have to rely on less than perfect answers when stepwise regression methods are employed. The performance of model 3 can only be properly evaluated after the data for the forecast period have become available. Electrical energy consumption models only are not enough for effective and efficient utility planning. Base demand models and weather sensitive demand models are also required. The future work is to develop base demand and weather sensitive demand models. Fig. 6. Plots of residuals obtained when electricity consumption is regressed against the price of electricity, the number of customers and the number of tourists.

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