Energy Sources, 26:463–472, 2004 Copyright © Taylor & Francis Inc. ISSN: 0090-8312 print/1521-0510 online DOI: 10.1080/00908310490429740
Forecasting Residential Natural Gas Demand HAYDAR ARAS Osmangazi University Department of Mechanical Engineering Bati Meselik, Eskisehir, Turkey
NIL ARAS Osmangazi University Department of Industrial Engineering Bademlik, Eskisehir, Turkey In Turkey, the major purpose of natural gas demand in residences is heating, where the residential gas usage is approximately 18% of total gas consumption. The studies on forecasting gas demand for future periods have a great importance because natural gas is an imported energy source. This article describes an approach to obtain appropriate models for forecasting residential monthly natural gas consumption. The method relies on dividing a year into two seasons as heating and non-heating periods and estimating individual autoregressive time series models for each period instead of attempting to capture the seasonal patterns in a single model. In the models, the dynamic relationships between natural gas consumption with time and degree-days measured by weather temperature variations are investigated. This method is applied to one of the five cities supplied by natural gas for residential use in Turkey. A comparison of the forecast accuracy of proposed procedure with that of a single model approach applied to the full set of observations is provided. The results reveal that to use separate models for each period reduces the forecast errors significantly when the major purpose of natural gas demand is space heating. Keywords residential natural gas consumption, time series, autoregressive model, degree days
Energy is one of the most important inputs required to maintain social and economical improvement in a country. It is necessary that energy demand should be performed at the right time economically, and be of good quality and respectful of increasing environmental consciousness in order to preserve national development and a high standard of living. The oil bottleneck of the early 1970’s that shook up the world economies has accelerated the escape from petroleum in the communication, energy production, industry and residential sectors. Thus, the world has turned towards seeking alternative energy sources. Natural gas has been used on a large scale by producer countries such as Russia, U.S.A. and the Middle East since the petroleum crisis and has also been taken into consideration by other Received 2 December 2002; accepted 25 February 2003. Address correspondence to Haydar Aras, Osmangazi Universitesi, Makina Muhendisligi Bolumu, TR26480, Bati Meselik, Eskisehir, Turkey. E-mail:
[email protected].
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countries as an alternative fuel because of its cleanliness, burning easiness, high thermal value and resource availability. The role of natural gas in energy supply is expected to increase in all regions of the world as a result of the increase in demand for energy. Using energy resources efficiently depends on predicting energy demand of the future accurately. Therefore, numerous researchers and practitioners have analyzed various energy issues and focused on developing appropriate energy demand models of which reduce the magnitude of forecast errors for more efficient consumption of energy sources since the oil crisis. There are several studies that used various methods to forecast residential consumption of natural gas in literature. Herbert (1987) has analyzed monthly natural gas sales to the residential consumers in America. Liu and Lin (1991) have used time series models to forecast residential natural gas consumption in Taiwan. Eltony (1996) has forecasted natural gas demand in Kuwait by means of econometric models. Smith, Husein and Leonard (1996) have used expert systems in decision making process when forecasting short term regional gas demand. Bartels, Fiebing and Nahm (1996) have benefitted from conditional demand analysis in order to determine gas demand. It seems that artificial neural networks have been used to forecast natural gas consumption in some studies (Hobbs et al., 1998; Brown, 1996). Knowles and Wirick (1998) have developed a portfolio optimization model for a natural gas distribution company. Durmayaz, Kadioglu and Sen (2000) have calculated the need for natural gas of a flat in Istanbul with different scenarios for different numbers of people by using the degree hours method. Gumrah et al. (2001) have forecasted natural gas demand using a model that depends on the degree-day concept for Ankara. Recently, considerable attention has been focused on the energy resources and energy investigating in Turkey. Natural gas is the fastest growing primary energy source and it is projected to increase dramatically in coming years (Yazici and Demirbas, 2001). A dynamic economy together with population growth, industrialization, rapid urbanization, source diversification and environmental concerns have brought natural gas usage into the energy scene in Turkey. This article aims at estimating reliable models to forecast residential consumption of natural gas. Natural gas is used for three main purposes in residences: space heating, cooking and providing hot water. Residential heating depends very much on weather variations, such as outside temperature, wind, and humidity. However, natural gas needs for cooking and hot water is slightly affected by climatic conditions throughout the year. The proposed approach in this paper involves dividing the gas demand time series into months in which heating is required (termed “heating period”) and months when heating is required a little or not at all (termed “non-heating period”), and then applying separate models for the two sets of data. In this study, the inferential time series models that employ a combination of the deterministic component of the typical multiple regression model with an autoregressive model are estimated by using monthly observations obtained from one city that is supplied with natural gas for residential use in Turkey. Natural gas consumption is modeled as a function of time and weather is expressed in terms of degree-day variables. A comparison of the forecast accuracy is also provided between proposed procedure and a single model approach. The results strongly support that forecasting errors are significantly reduced when using separate models.
The Status of Residential Usage of Natural Gas in Turkey In line with its strategic position, Turkey is a bridge between the natural gas production zones of the Middle East, Caspian Sea and the European Consumption market. Although Turkey is surrounded with countries that have the most productive reserves of oil and
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Figure 1. Natural gas consumption rates according to sectors in 2001 in Turkey.
natural gas of the world, natural gas is an imported energy source for Turkey where domestic gas reserves are too limited. Usage of natural gas has expanded sharply with the signing of the First Sales and Purchase Agreement with the former USSR in 1986. Turkey, as a growing natural gas market, can be divided into four main sectors: power generation, fertilizer, residential, and industrial. Currently, natural gas is supplied to five cities for residential use in Turkey. The major reason of natural gas demand in residential sector is heating, although it is also used for cooking and hot water. Transition to living with natural gas started in Ankara (the capital city of Turkey) in 1988, and continued respectively with Istanbul and Bursa in 1992, then Eskisehir and Izmit in 1996. Importation and distribution of natural gas to the cities is undertaken presently by BOTAS (Petroleum Pipeline Corporation). The city gas distribution companies are IGDAS in Istanbul, EGO in Ankara, BURSAGAZ in Bursa, ESGAZ in Eskisehir and IZGAZ in Izmit. The monopoly status of BOTAS has been abolished by the Natural Gas Market Law that was enacted on 2 May 2001 for the purpose of establishing a stable and transparent natural gas market based on the competitive rules. According to data obtained from BOTAS, Figure 1 gives the natural gas consumption rates for sectors in 2001 and Figure 2 shows the residential usage rates in total natural gas consumption versus years in Turkey. It seems that the residential sector is the second sector where natural gas is used mostly, with 18%. The residential demand percentages in total gas consumption have rapidly gone up from 1988 to 1998; however, a slight decrease has appeared in consumption after 1998, although the residential consumers
Figure 2. Natural gas consumption rates of the residential sector in years in Turkey.
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Figure 3. Total consumption amounts of natural gas (Million sm3 ) according to months in 1999– 2001 in Turkey.
that use natural gas have increased in each year. It can be said that this descent arises from higher average weather temperatures and economic crisis. Figure 3 presents the total amounts of consumed natural gas in residences according to months included in a three-year period between 1999 and 2001 in Turkey. The monthly observations data has been provided from city distribution companies (EGO, IGDAS, BURSAGAZ, ESGAZ, and IZGAZ). The effect of seasonal weather variations on the natural gas consumption is apparent. The total levels of gas consumption are large and rather changeable in the months between October and April, while the total amounts of gas usage show little and almost stable level in the months between May and September, relatively. It is evident that the major reason of using natural gas in residences is space heating. A year can be divided into two sections as heating and non-heating periods by considering whether natural gas is used intensively or not. The heating period starts from the beginning of October and continues to the end of April, including seven months in which heating appliances are used frequently. However, the non-heating period includes five months between May and September and natural gas is consumed sometimes or never with the intention of heating. It seems that natural gas has reached a maximum level of consumption in January and dropped to a minimum level in October during the heating period in total amounts over the three years. It is possible to observe the base consumption that is independent of weather condition during the non-heating period. However, it has drawn attention to the gas consumptions in September and May. These are transient months of autumn and spring, and natural gas can be used to heat residences, although rarely.
Building of Time Series Models There are several approaches to modeling time series with seasonality, which are discussed in the literature. If past studies are investigated, it appears that estimating a single
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model for the annual cycle of 12 months using all of the data is usual. Although it seems that using one model for the entire year is more efficient than using different models for each season, this way can result in large forecast errors and a more complicated model when the major purpose of natural gas usage is space heating. It is reasonable to divide a year into heating and non-heating periods. The amounts of gas consumption are rather different in two periods because principal intentions of gas usage are different. This study proposes to split the whole monthly data into two groups in which heating is a major purpose of gas consumption or heating is a minor purpose or not one at all of gas usage, then estimating different time series models for the two sets of data. Autoregressive time series models are estimated in this article to identify the nature of the phenomenon by the sequence of observations and to predict future values of the residential natural gas demand. Models are a combination of a multiple regression model and a first order autoregressive error model. Regression models are a function of time and degree day value and accounts for the trend and seasonal component of time series. The autoregressive model is for random components because when a regression analysis is applied to time series data, the residuals are frequently correlated. The residual correlation can be taken in a time series model, thereby improving both the fit of the model and the reliability of model inferences by using autoregressive models that are very flexible and receive more application in business forecasting (Mendenhall, 1996). The proposed approach was applied to data obtained from one of the five cities that use natural gas in Turkey. The data set contains monthly amounts of natural gas consumption in residences and daily mean temperatures for a 61-month period from December 1996 to December 2001 in Eskisehir. The monthly residential gas consumptions in sm3 (standard cubic meter) have been obtained from ESGAZ. The mean daily temperatures that were used to calculate monthly degree-day values have been provided by State Meteorology Office, Head of Eskisehir District, and the unit is in Centigrate. The monthly heating degree-day numbers are used as an indicator of weather variations in the study because weather temperature has a powerful effect on the natural gas consumption. The value of heating degree days for a month have been calculated as: DD =
Z
(Ti − Tdo )j
Degree Day
(1)
j =1
where Z: Ti : Tdo : DD:
The number of days in a month, Temperature of a heated place/internal temperature [◦ C], Daily average outdoors temperature [◦ C], Monthly degree day value.
In most countries Ti = 20◦ C and Tdo ≤ 15◦ C are accepted (Dagsoz, 1995). The value of 15◦ C is called base temperature and varies from one location to another. It affects the start date of space heating load in buildings. If the mean outside temperature is greater than the base temperature, there is no need for heating a building and the degree-day number is zero. When daily average temperature is 15◦ C or lower, degree-day is calculated for that day, while internal temperature is kept constant at 20◦ C. By adding up the daily temperature deficits over a month, cumulative degree days are obtained. Figure 4 is a scatter diagram of monthly natural gas consumptions versus monthly degree-day values for Eskisehir. As expected, the positive relationship between degree-days and gas consumption is quite clear. The higher degree-days cause increased consumption of natural gas because of greater usage of heating equipment; in contrast, lower degree-days yield
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Figure 4. The monthly natural gas consumptions (Million sm3 ) versus the monthly degree-day numbers for Eskisehir.
lower usage of natural gas. In Table 1, total amounts are given of the consumed natural gas per year and the annual heating degree-day numbers are obtained by aggregating the monthly values during a heating period. Total gas consumptions per year in heating and non-heating periods can be also seen individually in Figures 5a and 5b. Natural gas usage and the amount of gas has increased with the year, and between 94% and 98% of annual consumption has been used in heating period. The number of consumers has been increasing each year; however, the consumption level of 2001 has been lower than the year before. If Figures 5a and 5b are looked at carefully, it seems that this decrease is valid only in the heating period of 2001. Energy demand tends to vary according to how cold the weather is for heating. The annual degree-day value has decreased significiantly in 2001 and less natural gas has been needed to heat buildings. Increasing gas consumption with each year is more evident in non-heating periods. Three different models have been designed to consider possible relationships between response variable and explanatory variables in order to be able to select the model that displays the best performance for each data set. The first model in which natural gas consumption is a linear function of time, and degree-day value can be written as: Yt = β0 + β1 (t) + β2 (DD)t + φRt−1 + εt
(2)
Table 1 The consumptions of natural gas per year (Million sm3 ) and annual degree-day values for Eskisehir Years Natural gas consumption Annual degree-day number
1996 4.55 466
1997
1998
1999
2000
2001
47.19
55.09
74.53
96.79
84.62
3402.60
3022.50
2930.80
3376.40
2855.30
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Figure 5. Natural gas consumptions (Million sm3 ) per year in Eskisehir (a) in heating periods (b) in non-heating periods.
where Yt : Natural gas consumption [sm3 ] in month t, t: Time index corresponds to observed month, t = 1, 2, . . . , 61 (t = 1 for December 1996 and t = 61 for December 2001) DDt : Degree day value for month t, Rt : Residual component, Rt = φRt−1 + εt , −1 < φ < 1, Rt−1 : Previous residual value, and εt is uncorrelated error. The numerical value of φ determines the sign (positive or negative) and strength of the autocorrelation. The value of the coefficient βi determines the contribution of the independent variable Xi , given that the other (k − 1) independent variables are held constant, and β0 is the y-intercept. The relationship between dependent and independent variables is assumed as non-linear in the second1 and third2 models. However, the second 1 Second model, Y = eβ0 .eβ1 (t) .eβ2 (DD)t .eRt t 2 Third model, Y = β(t)β1 (DD)β2 e(Rt ) t t
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and third models can be written in the following form of an additive model by taking natural logarithms of both sides: LNY t = β0 + β1 (t) + β2 (DD)t + φRt−1 + εt
(3)
LNY t = β0 + β1 LN(t) + β2 LN(DD)t + φRt−1 + εt
(4)
A performance comparison has been provided to determine whether or not it may be more adequate to apply separate models for each period instead of a single model. In order to evaluate the performance among different models, mean absolute error (MAE), mean absolute percentage error (MAPE), and mean squared error (MSE) have been employed using concerned data. Forecast Using a Single Model for a Year To make a comparison of the forecast accuracies between the suggested and usual procedures, time series models in three different forms have been built for 12 months by using a full set of 61 observations. The estimation results are given in Table 2. The utility test that is done for significance level 1% shows that there is sufficient evidence to indicate that models are statistically useful for predicting gas consumption. When the table results are compared it seems that the first model displays the best performance for a full data set, according to MAE and MSE. However, pay attention to high values of MAPE, in particular to that of first model. MAPE provides an indication of how large the forecast errors are in comparison to the actual values of the series. When the monthly forecast values were analyzed one by one for three models, it was realized that the large percentage errors resulted from great differences between observed and forecast values of the months in which less natural gas was consumed. The high values in the percentage deviations of forecasts cause a suspicion on reliability of the models and encourages to obtain appropriate models by applying different models after splitting the data set into two groups. Forecast Using Two Separate Models for a Year The overall data set consists of 36 observations included by heating periods plus 25 observations included by non-heating periods. Three different models of time series have been constructed with both heating and non-heating seasons, and individual models have been chosen by comparing forecast errors. The estimated results are given in Table 3. The last line of the table are the calculated average values for a year when separate models are used for each period. A comparison made between the results of Tables 2 and 3 show
Table 2 The estimation results of the single model approach Model No.
MAE
MAPE
MSE
1 2 3
1098709.63 2849990.90 2105045.27
2.25 0.43 0.90
2.0360409 E+12 7.5228987 E+13 8.6230583 E+12
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Table 3 The estimation results of the proposed approach Model
MAE
MAPE
MSE
Heating period Non-heating period For a year
961557.53 125719.92 619001.13
0.12 0.23 0.16
1.3689108 E+12 6.3323453 E+10 8.3383402 E+11
that a very impressive decrease in forecast errors is provided. It is noted that the mean forecast deviation from actual value is ± 16 is a very reasonable value. The model (5), in which both response and explanatory variables are indicated by using logarithm transform, seems the most adequate model for the heating period; however, the model (6), in which only the response variables are denoted by using logarithm transform, appears the most suitable model for the non-heating period. The numbers in the parenthesis indicate t-values and standard errors of estimated model coefficients. Models are statistically useful for predicting gas consumption according to utility test at significance level 1%. Besides, the high values of t-statistics imply that independent variables are statistically significant in the models. LNYˆ t = 7.090167 + 0.320217LN(t) + 1.303985LN(DD)t + 0.08910131Rt−1 t-value (13.335) std. error (0.5317)
(10.540) (0.0304)
(15.665) (0.0832)
LNYˆ t = 10.819272 + 0.043988(t) + 0.012157(DD)t − 0.12192605Rt−1 t-value (75.127) std. error (0.1440)
(12.643) (0.00348)
(5)
(6)
(10.639) (0.00114)
The multiple coefficients of determination (R 2 ) are 0.91 for both periods. The models appear to fit the data quite well with high values of R 2 that imply the accountability rate of the model. Approximately 91% of the sample variation in natural gas consumption is explained by the first order autoregressive models, both in heating and non-heating periods.
Conclusions This paper describes an approach to obtain appropriate models that will assist in the economic planning and supplying of natural gas to residences by providing short term forecasts. The proposed procedure is dividing a year into heating and non-heating periods and then estimating individual models for each period, if the principal purpose of gas consumption is space heating. The yield is a significant decrease in forecast errors. Applied models are first order autoregressive time series models in which the deterministic component is a periodic function of time and degree-day values. Results of the study demonstrate that the models are valid for forecasting, and future consumptions of natural gas can be obtained much more accurately by means of this proposed approach. Residential consumption of natural gas has been gradually increasing in Turkey. It is important to use accurate forecasting models to solve imbalances between
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demand and supply, particularly in winter months. Even though the gas demand seems stable during summer months, it is worth building a forecasting model not only for the heating period but also the non-heating period because natural gas is an imported energy source for Turkey in which there are not sufficient enough gas storage facilities. Forecasts over or under the necessity can result in heavy economical losses. Efforts aimed at the development and implementation of reliable forecasting techniques will be justified on the basis of large savings.
References Bartels, R., D. G. Fiebig, and D. Nahm. 1996. Regional end use gas demand in Australia. The Economic Record 72:319–331. Brown, R. H. 1996. Development of artificial neural networking models to predict daily gas consumption. Am. Gas Assoc. Forecasting Rev. 5:1–22. Dagsoz, A. K. 1995. Degree day values in Turkey, national energy saving policy, heat insulation in buildings. Istanbul: Izocam. Durmayaz, A., M. Kadioglu, and Z. Sen. 2000. An application of the degree-hours method to estimate the residential heating energy requirement and fuel consumption in Istanbul. Energy 25:1245–1256. Eltony, M. N. 1996. Demand for natural gas in Kuwait: An empirical analysis using two econometric models. International Journal of Energy Research 20:957–963. Gumrah, F., D. Katircioglu, Y. Aykan, S. Okumus, and N. Kilincer. 2001. Modeling of gas demand using degree day concept: case study for Ankara. Energy Sources 23:101–114. Herbert, J. H. 1987. An analysis of monthly sales of natural gas to residential customers in the United States. Energy System and Policy 10:127–147. Hobbs, B. F., U. Helman, and S. Jitprapaikulsarn. 1998. Artificial neural networks for short term energy forecasting: Accuracy and economic value. Neurocomputing 23:71–84. Knowles, T. W. and J. P. Wirick. 1998. The peoples gas light and coke company plans gas supply. Interfaces 28:1–12. Liu, L. M. and M. W. Lin. 1991. Forecasting residential consumption of natural gas using monthly and quarterly time series. International Journal of Forecasting 7:3–16. Mendenhall, W. and T. Sincich. 1996. A second course in statistics: Regression Analysis. New Jersey: Prentice Hall, Inc. Smith, P., S. Husein, and D. T. Leonard. 1996. Forecasting short term regional gas demand using an expert system. Expert Systems with Applications 10:265–273. Yazici, N. and A. Demirbas. 2001. Turkey’s natural gas necessity and consumption. Energy Sources 23:801–808.
