Determination of the Parameters That Effect the Residential Electrical Consumption of Turkey

Transcription

1 AASCIT Journal of Energy 2017; 4(4): ISSN: (Print); ISSN: (Online) Determination of the Parameters That Effect the Residential Electrical Consumption of Turkey Önder Güler *, Sezgin Durak Energy Institute, Istanbul Technical University, Istanbul, Turkey address (Ö. Güler) * Corresponding author Keywords Residential Electrical Energy Consumption, Survey, Regression Models, Environmental Consciousness Received: August 16, 2017 Accepted: November 22, 2017 Published: December 18, 2017 Citation Önder Güler, Sezgin Durak. Determination of the Parameters That Effect the Residential Electrical Consumption of Turkey. AASCIT Journal of Energy. Vol. 4, No. 4, 2017, pp Abstract In this study, the factors, which effects the residential electrical consumption, are investigated. Hypotheses for the explanation of the relationship between the residential electric bill and other factors, which are family average income, residence size, ratio of electricity used in heating, family size, city of residence, family average age, age of the people who filled the survey, education level, are created. For the determination of the parameters, which effect the use of the residential electricity, a survey is created by using the results of the studies, and this survey is put on internet. Validity of the hypotheses are tested by making statistical tests using the survey data. Regression models are created for the prediction of mean residential electricity bill and electricity bill quota per capita by using the results of the tests. Variety of analyses are made such as; age and income analysis for the determination of the user characteristics, environmental consciousness analysis in terms of electrical consumption, electricity savings analysis. Also, the effect of the electric bill on electricity savings is analysed. 1. Introduction Electrical energy is used widely, because of using easily, wide distribution network, and being a non-polluting energy. The installed electrical energy capacity of Turkey is MW, and total annual production is TWh in 2015 [1]. From 1970 onwards, the demand for electrical energy increased with an 8.2% annual mean, except the years 2001 and 2009, when the demand actually decreased because of the economic crises. As can be understood from the annual electrical production growth, making new investments annually for the production of the electrical energy is inevitable. However, supply of the electrical energy depends on long term solutions. Thus, for the prevention of the problems related to the electrical energy in the long term, determination of the future electrical energy demand is of utmost importance. Within this context, the reasons of the electrical energy demand must be investigated, the most prominent factors must be determined, and the necessary steps must be taken. One of the places where the electrical energy is consumed, is the residential buildings. There are annual increases at the residential electrical consumptions. For this reason, in the scope of this study, the factors effecting the residential electrical consumption, which constitutes a big part of the total electrical consumption of Turkey, are investigated. Studies are conducted across the world for the determination of the factors that affect the residential electrical consumption. As a result of the studies, the factors at the residential electrical consumption such as household total income, household size, resident size, areas at which the electricity is used, price of the electricity etc. are found to be important. A

2 20 Önder Güler and Sezgin Durak: Determination of the Parameters That Effect the Residential Electrical Consumption of Turkey study about the residential electrical consumption at California shows that household income, the age of the head of the family, the size of the family, the size of the residence, and the areas at which electricity is used, are found to have important roles at the residential electrical consumption characteristics [2]. A study on Utah laid emphasize on the hypotheses, which states that the life on the rural and urban areas may create a meaningful difference on the residential electrical consumption. The study showed that, the factors such as the variety and the number of the electrical devices used in the residence, number of family members that live in the residence, the age of the head of the family, etc. may be different for rural and urban areas, and that may affect the residential electrical consumption [3]. In another study conducted on 400 electric consumers at Florida at 1991, factors that effect the usage of the electricity had been searched. The most important factor at the consumption of the electricity is found to be residential heating, however other factors such as the family size, the income, the age of the residence, etc are also found to be important [4]. When several studies are taken into account, it can be seen that the factors such as income, number of family members, residence size, family average age, educational level, usage of electricity for heating etc are important at the residential electrical consumption. There are so many studies about this subject in different country [2-10]. In this study, the factors, which effects the residential electrical consumption, are investigated. In the first part of the study, hypotheses for the explanation of the relationship between the residential electric bill and other factors, which are family average income, residence size, ratio of electricity used in heating, family size, city of residence, family average age, age of the people who filled the survey, education level, are created. In the second part of the study, for the determination of the parameters, which effect the use of the residential electricity, a survey is created by using the results of the studies, and this survey is put on internet. At the third part, validity of the hypotheses are tested by making statistical tests using the survey data. At the fourth part, regression models are created for the prediction of mean residential electricity bill and electricity bill quota per capita by using the results of the tests. At the fifth part, a variety of analyses are made. Some of these are, age and income analysis for the determination of the user characteristics, environmental consciousness analysis in terms of electrical consumption, electricity savings analysis. Also, the effect of the electric bill on electricity savings is analysed. 2. Hypotheses For the determination of the electric usage characteristics of the residential consumers, hypotheses for the relationship between the residential electric bill and other factors, which are mean family income, residence size, ratio of electricity used in heating, family size, city of residence, mean family age, age of the people who filled the survey, education level, are created. Also, for the evaluation of environmental consciousness, hypotheses 9 and 10 are created, which looks to the electrical consumption s relationship with age and income. In Table 1, hypotheses used for the determination of electrical consumption are given. Table 1. Hypotheses used for the determination of electrical consumption characteristic. Hypothes No Description of hypothesis 1-H0 There is no relationship between the family mean income and residential electric invoice cost 1-H1 There is a relationship between the family mean income and residential electric invoice cost 2-H0 There is no relationship between the residence size and residential electric invoice cost 2-H1 There is a relationship between the residence size and residential electric invoice cost 3-H0 There is no relationship between the usage ratio of electricity for heating and residential electric invoice cost 3-H1 There is a relationship between the usage ratio of electricity for heating and residential electric invoice cost 4-H0 There is no relationship between the family member size and residential electric invoice cost 4-H1 There is a relationship between the family member size and residential electric invoice cost 5-H0 There is no relationship between the city of residence and the residential electricity bill 5-H1 There is a relationship between the city of residence and the residential electricity bill 6-H0 There is no relationship between the family average age and residential electric invoice cost 6-H1 There is a relationship between the family average age and residential electric invoice cost 7-H0 There is no relationship between the age of the people filled the survey and residential electric invoice cost 7-H1 There is a relationship between the age of the people filled the survey and residential electric invoice cost 8-H0 There is no relationship between the education level and residential electric invoice cost 8-H1 There is a relationship between the education level and residential electric invoice cost 9-H0 There is no relationship between the age and paying attention to electrical consumption in terms of environmental consciousness 9-H1 There is a relationship between the age and paying attention to electrical consumption in terms of environmental consciousness 10-H0 There is no relationship between the income and paying attention to electrical consumption in terms of environmental consciousness 10-H1 There is a relationship between the income and paying attention to electrical consumption in terms of environmental consciousness 3. Survey A survey for the determination of the parameters, which effect the residential electrical energy consumption, is created by using study results, and submitted to the internet environment. Survey was released at March 1, 2012, and as of March 16, 2012, it was answered by 913 people. However, 101 of these surveys are eliminated with the reasons given below, and analyses are done using the remaining 812 survey data.

3 AASCIT Journal of Energy 2017; 4(4): The reason for the elimination of the survey data are: People who conducted survey from abroad (For example, Germany), Filling survey with wrong/improbable data (For example, size of family is 215), Missing data (For example, there is no data bill), Unrealistic data (For example, despite the electricity being the source of all of the residential heating, a mere 30 TL electricity bill), Extreme data (Such as 800 TL electricity bill). Some of the properties of the sampling mass of the surveyors are given at Figure 1. a. Distribution of the living city b. Distribution of the age c. Distribution of the people number in a family d. Distribution of the size of the home e. Distribution of the monthly average income f. Histogram of the average electric bill Figure 1. Some of the properties of the sampling mass of the surveyors. Surveyors were participated from 56 cities of Turkey. However, as can be seen from Figure 1a, participation from Istanbul and Ankara is more than the participation from all of the other cities. Also, releasing the survey from the internet is another factor that decreased the participation from other cities. Age distribution of the participants can be seen at Figure 1b. As can be seen from age distribution, the number of participants between the age 50 and 60 is more than expected, which prevents the distribution from conforming to normal distribution. The reason for high number of participants for this age group may be that, they have a higher will to spend time for completing the survey. As the

4 22 Önder Güler and Sezgin Durak: Determination of the Parameters That Effect the Residential Electrical Consumption of Turkey participants with age represent working people, it conforms to normal distribution. Observation of family size of the participants, which is given at Figure 1c, shows that family size conforms to normal distribution. Observations show that, 11% of the participants live alone. The highest family size is 8, and can be seen at the column that shows the participants with a family size of 5 or higher. Inspection of Figure 1d, which shows the size of residences of the participants, reveal that residence sizes conform to normal distribution. About 70% of the participants live in residences with an area of 80 to 140 m 2. Inspection of the family mean income given at Figure 1e shows that, these incomes conform to normal distribution. Inspection of Figure 1f, which is prepared by taking December 2011, January 2012, and February 2012 mean bills into consideration, shows that bills conform to normal distribution. Generally, all of the data conforms to normal distribution. One of the biggest reason for that is the size of the sampling mass (812 people). 4. Tests 4.1. Testing the Relationship Between Income Distribution Groups and Paid Invoice Costs As a result of the literature survey, income distribution, which is thought to be related to the residential electrical consumption, is put into ANOVA test. First of all, the test of the homogeneity of variance of the income distribution groups in terms of the paid invoice costs is carried out. The test result is given at Table 2. Table 2. Testresults of homogeneity of variance of the income distribution groups in terms of the paid invoice costs As the test result is less than 0.05, it can be said that the variances are not distributed homogeneously. Thus, one of the tests between welch test and a type of post hoc test by taking nonhomogeneous variance distribution into consideration is chosen. For this, Games-Howell test is chosen. Table 3. Test results of income distribution significant differences between the groups in terms of the invoice value. Statistic df1 df2 Significance Welch Inspection of Table 3 shows that, as the level of importance is less than 0.05, in terms of paid invoice costs with respect to income distribution, there are meaningful difference between at least two groups. In other words, 1-H0 hypothesis (there is no relationship between the family mean income and residential electric invoice cost) is rejected and 1-H1 hypothesis (there is a relationship between the family mean income and residential electric invoice cost) is accepted. Games-Howell test results, which inspects meaningful differences between all of the groups, is given at Table 4. Table 4. Test results of income groups with statistically meaningful differences amongst the mean invoice costs. Compared income groups Average difference Standard error Significance TL TL * TL TL * TL >10000 TL * TL TL * TL TL * TL TL * TL TL * TL >10000 TL * TL * TL >10000 TL * * Difference is meaningful in smaller than 0.05 significance level At Table 4, income groups with statistically meaningful differences amongst the mean invoice costs are given. The importance levels calculated for difference levels are less than Group couples without a meaningful difference are not given at the table. The main reason for a specific income group s, which has less than 1500 TL income, lack of meaningful difference with another group may be its small sampling mass. As a result of this, its standard error may be higher. However, inspection of all of the other groups show that, going from lower income group to higher income group increases the meaningful differences. In short, income level effects paid invoice costs in a meaningful way, and paid invoice costs increase as the income increase Testing the Relationship Between Residence Size Groups and Paid Invoice Costs As a result of the literature survey, residence size distribution, which is thought to be related to the residential electrical consumption, is put into ANOVA test. First of all, the test of the homogeneity of variance of the residence size distribution groups in terms of the paid invoice costs is carried out. The test result is given at Table 5. Table 5. Test results of the homogeneity of variance of the residence size distribution groups in terms of the paid invoice costs

5 AASCIT Journal of Energy 2017; 4(4): As the test result is more than 0.05, it can be said that the variances are distributed homogeneously. Thus, one of the tests between ANOVA test and a type of post hoc test by taking homogeneous variance distribution into consideration is chosen. For this, Tukey test is chosen. Table 6. Test results of variance of the residence size distribution groups with statistically meaningful differences amongst the mean invoice costs. Between groups Within groups Total Inspection of Table 6 shows that, as the level of importance is less than 0.05, in terms of paid invoice costs with respect to residence size, there are meaningful difference between at least two groups. In other words, 2-H0 hypothesis (there is no relationship between the residence size and residential electric invoice cost) is rejected and 2-H1 hypothesis (there is a relationship between the residence size and residential electric invoice cost) is accepted. Tukey test results, which inspects meaningful differences between all of the groups, is given at Table 7. At Table 7, residence groups with statistically meaningful differences amongst the mean invoice costs are given. The importance levels calculated for difference levels are less than Group couples without a meaningful difference are not given at the table. At Table 7, it can be seen that there is a statistically meaningful difference between the electrical bills of a family with less than 60 m 2 residence size and the electrical bills of a family with more than 160 m 2 residence size. There is also a meaningful difference between the bills of a residence with a size of m 2 and a residence with a size more than 140 m 2. Table 7. Residence size groups with statistically meaningful differences amongst the mean invoice costs. Compared residence groups Average Standar Significanc difference d error e <60 m m * <60 m 2 >180 m * m m * m m * m m * m 2 >180 m * m m * m m * m m * m 2 >180 m * m m * m 2 >180 m * * Difference is meaningful in smaller than 0.05 significance level Mean invoice costs increase continuously while going from the residence with the smallest size to the residence with the biggest size. However, as can be seen from Table 7, because of the big standard error, meaningful differences occurred with the size of the difference of the mean values. On the other hand, meaningful difference between the bill means of the residences with m 2 and m 2, which are very close in terms of size, can be explained with low standard error, or in other words, with the size of the sampling mass. In short, residence size effects invoice costs meaningfully, and invoice cost increases as the residence size increases Testing the Relationship Between Heating Type Groups and Paid Invoice Costs As a result of the literature survey, heating type (usage ratio of electricity for heating) distribution, which is thought to be related to the residential electrical consumption, is put into ANOVA test. First of all, the test of the homogeneity of variance of the heating type distribution groups in terms of the paid invoice costs is carried out. The test result is given at Table 8. Table 8. Test results of the homogeneity of variance of the heating type distribution groups in terms of the paid invoice costs As the test result is more than 0.05, it can be said that the variances are distributed homogeneously. Thus, one of the tests between ANOVA test and a type of post hoc test by taking homogeneous variance distribution into consideration is chosen. For this, Tukey test is chosen. Table 9. Test results of variance of the heating type distribution groups with statistically meaningful differences amongst the mean invoice costs. Between groups Within groups Total Inspection of Table 9 shows that, as the level of importance is less than 0.05, in terms of paid invoice costs with respect to heating type, there are meaningful difference between at least two groups. In other words, 3-H0 hypothesis (there is no relationship between the usage ratio of electricity for heating and residential electric invoice cost) is rejected and 3-H1 hypothesis (there is a relationship between the usage ratio of electricity for heating and residential electric invoice cost) is accepted. Tukey test results, which inspects meaningful differences between all of the groups, is given at Table 10.

6 24 Önder Güler and Sezgin Durak: Determination of the Parameters That Effect the Residential Electrical Consumption of Turkey Table 10. Heating type groups with statistically meaningful differences amongst the mean invoice costs. Compared heating Average Standard type groups difference error Significance % 0 % 50-9,973 * 2,574 0,001 % 0 % ,860 * 4,704 0,000 % 25 % ,141 * 4,999 0,000 % 50 % ,887 * 5,047 0,004 * Difference is meaningful in smaller than 0.05 significance level Heating type groups with statistically meaningful difference with mean invoice costs are given at Table 10. Importance levels calculated for difference values are less than Group couples without a meaningful difference are not given at the table. The first meaningful difference with the group that do not use electrical energy at all for heating starts with the group that use electrical energy for half of the heating. There is not a meaningful difference between the groups that come one after another. There is not a meaningful difference between the group that uses electricity for all of the heating and the group that uses electricity for most of the heating. In short, the type of heating effects invoice costs in a meaningful way, and invoice costs increase as the usage ratio for electricity for heating increase Testing the Relationship Between Family Member Size Groups and Paid Invoice Costs As a result of the literature survey, family member size distribution, which is thought to be related to the residential electrical consumption, is put into ANOVA test. First of all, the test of the homogeneity of variance of the heating type distribution groups in terms of the paid invoice costs is carried out. The test result is given at Table 11. Table 11. Test results of the homogeneity of variance of the family member size groups in terms of the paid invoice costs As the test result is more than 0.05, it can be said that the variances are distributed homogeneously. Thus, one of the tests between ANOVA test and a type of post hoc test by taking homogeneous variance distribution into consideration is chosen. For this, Tukey test is chosen. Table 12. Test results of variance of the family member size groups with statistically meaningful differences amongst the mean invoice costs. Between groups Within groups Total Inspection of Table 12 shows that, as the level of importance is less than 0.05, in terms of paid invoice costs with respect to family member size, there are meaningful difference between at least two groups. In other words, 4-H0 hypothesis (there is no relationship between the family member size and residential electric invoice cost) is rejected and 4-H1 hypothesis (there is a relationship between the family member size and residential electric invoice cost) is accepted. Tukey test results, which inspects meaningful differences between all of the groups, is given at Table 13. Table 13. Family member size groups with statistically meaningful differences amongst the mean invoice costs. Compared family Average Standard number size groups difference error Significance * * * ve * * * ve * * ve * * Difference is meaningful in smaller than 0.05 significance level Family member size groups with statistically meaningful difference with mean invoice costs are given at Table 13. Importance levels calculated for difference values are less than Group couples without a meaningful difference are not given at the table. It can be seen that, a family with just one member pays invoice costs meaningfully different than other family groups. Inspection of other groups show that, there is no meaningful difference between the groups with a family member size of 4 or more. In short, family member size effects paid invoice costs in a meaningful way, and as family member size increases, invoice cost increases Evaluation of the Factors That Effect Invoice Costs According to the results of the study, four factors are found to be influential on December 2011, January 2012, and February 2012 invoice costs with respect to the data of 812 surveyors. These are: Income, Residence size, Type of heating, Family member size. Other factors are also tested. However, these factors given below are not found to have a meaningful effect on the invoice costs. Family average age, Age of the surveyed people, City of residence, Education level. There may be two reasons for these four factors not to be meaningful. One maybe that, they really do not affect the

7 AASCIT Journal of Energy 2017; 4(4): outcome. Another reason maybe that, the tally for creating meaningful difference between the groups may not be summed up. For example, 95.6% of the people surveyed are university graduates. The reason for this situation maybe that, the survey was conducted on the internet, and university graduates use internet actively. ANOVA test results of city of residence, which is one of the factors that do not affect the outcome, is given at Table 14 and 15 (given as Istanbul, Ankara, and the others). Table 14. Test results of the homogeneity of variance of the city of residence groups in terms of the paid invoice costs Table 15. Test results of variance of the city of residence groups with statistically meaningful differences amongst the mean invoice costs. Between groups Within groups Total The importance level is bigger than 0.05 as can be seen from Table 15, thus ANOVA test results can be used. As the importance level is higher than 0.05, it can be concluded that there is no meaningful difference between the groups. In other words, 5-H1 hypothesis (there is a relationship between the city of residence and the residential electricity bill) is rejected, and 5-H0 hypothesis (there is not a relationship between the city of residence and the residential electricity bill) is accepted. Similar results can be obtained for the other three factors that have no effect, namely average family age (Hypothesis no 6), age of the people filled the survey (Hypothesis no 7), and the education level (Hypothesis no 8). The reason for the average family age to be meaningless may be that, as there are a lot of age difference between the members of the family, average age may not mean anything. For example, average age of a family with two children and two parents, who are both at the age of 35, maybe 24, while a two member family without children also maybe 24. This situation may make average family age unimportant at the determination of the electrical consumption behaviour. Age of the people that filled the survey may be important at invoice costs. However, for it to happen, the people who filled the survey must be the leader of the family. In other words, she/he must be the decision maker. The reason for the city of residence to be meaningless may be the similar situation in the case of level of education. Generally, survey is conducted under three groups. These are Istanbul, Ankara, and other cities. A factor that differentiate electrical consumption between these three groups could not be determined. 5. Construction of the Prediction Model 5.1. Construction of the Mean Residential Electric Bill Prediction Model As a result of the survey, which took electric bills of December 2011, January 2012, and February 2012 into consideration, 4 factors that affect the electric bill is determined. These factors are: Income, Residence size, Type of heating, Family member size. A regression model that predicts electric bill using these 4 factors is created. Determinant coefficient of the regression model is given at Table 16. Table 16. Determinant coefficients of the regression model for predicting electric bill. R R square Adjusted R square Std. error of the estimation a a. predictors: (constant), heating, family number size, income and residence size Determination coefficient is found to be It means that, the independent variables can explain 33.3% of the variance of the dependent variable. As the residential electrical consumptions at micro scales depend on human behaviour, it is a very hard to predict variable. Two human with the same independent variables may consume electricity with totally different magnitudes. This situation is a topic in Table 17. Significance values of regression models for predicting electric bill. the social study field. Nevertheless, the created model may guide the prediction of the micro size residence electrical consumption behaviours. Importance value, which is the indicator showing if the resultant model is statistically important or not, as given at Table 17 is found to be As this value is less than 0.05, the resultant model can be said to be statistically meaningful. Regression Residual Total The explanatory power values of the coefficients of the multiple regression model, which are obtained for the prediction of

8 26 Önder Güler and Sezgin Durak: Determination of the Parameters That Effect the Residential Electrical Consumption of Turkey the average residental electric bills, for meaningfulness and dependent variable can be seen at Table 18. Importance values of all of the variables are less than 0.05, and they are statistically meaningful. Table 18. Coefficients of the regression model for predicting electric bill. Unstandardized coefficients Standardized coefficients B Standard error Beta t significance Constant Family number size Income Residence size Heating When beta values of the independent variables of the constructed model are inspected, it can be seen that family member size has the highest relationship with the dependent variable, whereas income distribution has the lowest relationship with the dependent variable. The low effect of income distribution may indicate that electrical energy is not a luxury product. According to the model given above, the average electricity bill of December, January, and February of a family that has less than 1500 TL income (1 x 3.558), has a residence size less than 60 m 2 (1 x 4.619), makes residential heating without using electricity (1 x 7.009), and has just one family member (1 x 7.209) will be about 30 TL Construction of the Average Residential Electric Bill per People Prediction Model At section 5.1, a regression model that considered the average electric bills of the residences was constructed. However, besides the determination of the residential electric bills, the determination of the electric bills per people is also important. For this reason, average bills collected with the help of the survey is divided to the family member size, and bills per people is found. Determinant coefficient is found to be 0.408, which is given at Table 19. The explanatory power of the variables for the electric bills per people is found to be 40.8%. This value is higher than the determinant coefficient calculated for mean invoice cost. As a result, the explanatory power of the existing independent variables for the invoice cost per people is higher than for the average invoice cost per residence. Table 19. Determinant coefficients of the regression model for predicting electric bills per people. R R square Adjusted R square Std. error of the estimation a a. predictors: (constant), heating, family number size, income and residence size Importance value, which is the indicator showing if the resultant model is statistically important or not, as given at Table 20 is found to be As this value is less than 0.05, the resultant model can be said to be statistically meaningful. Table 20. Significance values of regression models for predicting electric bills per people. Regression 74753, , ,766 0,000 Residual , ,762 Total , The explanatory power values of the coefficients of the multiple regression model, which are obtained for the prediction of the electric bills per people, for meaningfulness and dependent variable can be seen at Table 21. Importance values of all of the variables are less than 0.05, and they are statistically meaningful. Table 21. Coefficients of the regression model for predicting electric electric bills per people. Unstandardized coefficients Standardized coefficients B Standard error Beta t significance Constant Family number size Income Residence size Heating When beta values of the independent variables of the constructed model are inspected, it can be seen that family member size has the highest relationship with the dependent variable, whereas income distribution has the lowest relationship with the dependent variable. The same result had been obtained at the prediction of the average electrical consumption. However, in this model, the effect of the family member size is increased, while the effect of the other coefficients is decreased. According to the model given above, the average electricity bill of December, January, and February of a family that has less than 1500 Turkish Lira (TL) income (1 x 1.248), has a residence size less than 60 m 2 (1 x 1.436), makes residential heating without using electricity (1 x 3.297), and has just one family member (1 x 7.638) will be about 33 TL. Increasing the

9 AASCIT Journal of Energy 2017; 4(4): family size by one member in the very same family will decrease electric bills per people about 8 TL. 6. Determination of the Behaviour of the Users for the Saving of Electrical Energy Determination of the behaviour of the users for the consumption of electrical energy is an important issue. Especially, with the determination of the people who are environmentally conscious and make savings, other people that lack these attributes can be educated, their awareness can be raised, and, as a result, a decrease of the electrical consumption can be achieved. The relationship between the electrical consumption behaviour of the participants with respect to their age and income distribution was obtained. While making analysis with respect to age, two of the participants with ages less than 20 are included in the age group between 20 and 25. First of all, the test of the homogeneity of variances of age groups in terms of environmental consciousness is carried out. Test result is given at Table 22. Table 22. The test results of the homogeneity of variances of age groups in terms of environmental consciousness As the test result is less than 0.05, it can be said that the variances are not distributed homogeneously. Thus, one of the tests between welch test and a type of post hoc test by taking nonhomogeneous variance distribution into consideration is chosen. For this, Games-Howell test is chosen. Table 23. Test results of variance of the age groups with statistically meaningful differences amongst the environmental consciousness. Welch Inspection of Table 23 shows that, as the level of importance is less than 0.05, in terms of age groups with respect to environmental consciousness, there are meaningful difference between at least two groups. In other words, 9-H0 hypothesis (there is no relationship between the age and paying attention to electrical consumption in terms of environmental consciousness) is rejected and 9-H1 hypothesis (there is a relationship between the age and paying attention to electrical consumption in terms of environmental consciousness) is accepted. Games-Howell test results, which inspects meaningful differences between all of the groups, is given at Table 24. Table 24. Age groups that have statistically meaningful difference amongst their environmental consciousness values. Compared age groups Average Standard difference error Significance ,694 * 0,162 0, ,924 * 0,160 0, ,751 * 0,150 0, >60-0,854 * 0,172 0, ,590 * 0,136 0, ,417 * 0,124 0, >60-0,520 * 0,150 0, ,540 * 0,141 0,005 * Difference is meaningful in smaller than 0.05 significance level Age groups that have statistically meaningful difference amongst their environmental consciousness values are given at Table 24. The importance levels calculated for difference values are less than Group couples without a meaningful difference are not put into the Table. Generally, groups are divided into two at the age of 40. The first group consists of people with age 20-40, and in this group there is no meaningful difference in terms of environmental consciousness. The same situation is valid for the second group, and there is no meaningful difference in terms of environmental consciousness. Environmental consciousness of the first group (with the lower age) is less than the second group in a statistically meaningful manner. In short, environmental consciousness is effected by age in a meaningful way, and environmental consciousness increases as the age increases. Later on, homogeneity of variance test of the income distribution groups in terms of environmental consciousness is carried out. Test result is given at Table 25. Table 25. Test results of the homogeneity of variance of the income distribution groups in terms of environmental consciousness As the test result is found to be bigger than 0.05, it can be said that variances are distributed homogeneously. Thus, ANOVA test is carried out. Table 26. Test results of variance of the income distribution groups with statistically meaningful differences amongst the environmental consciousness. Between groups 2, Within groups Total The importance level is bigger than 0.05 as can be seen from Table 24. As the importance level is higher than 0.05, it can be concluded that there is no meaningful difference between income groups in terms of environmental consciousness. In other words, 10-H1 hypothesis (there is a relationship between the income and paying attention to electrical consumption in terms of environmental consciousness) is rejected, and 10-H0 hypothesis (there is no relationship between the income and paying attention to electrical consumption in terms of environmental consciousness) is accepted. There is no difference at environmental consciousness in terms of income.

10 28 Önder Güler and Sezgin Durak: Determination of the Parameters That Effect the Residential Electrical Consumption of Turkey At Figure 2, electrical energy saving methods used by 812 participants can be seen. The most preferred energy saving method is the use of energy saving lamps, while the less preferred method is taking the hours at when the electricity is cheaper into consideration. Again, the use of other devices that saves electricity is as common as the use of saving lamps. There may be several reasons for using these two types of energy saving methods: Practicality of use, Information on the devices intended at electricity savings Cheap to buy Figure 2. Electrical energy saving methods used by 812 participants. 7. Conclusions and Policy Implications In the scope of the study, factors that affect the residential electrical consumption at micro scale are determined. A survey is conducted, and with the answers of 812 participants, data are obtained and statistical analyses are done on these data. Determination coefficient is found to be The main reason for this situation is that, analyses at micro scale is strongly dependent on human behaviour. Despite several theses that tries to predict human behaviour, high success should not be expected. Especially for a phenomenon such as electrical energy, which is not hard to obtain, predicting human behaviour is not easy. However, in this study, some predictions are made with the help of the factors excluding human behaviour. Using the model, which takes average electric invoice costs of the months December 2011, January 2012, and February 2012 into consideration, average family income, family member size, size of the residence, and usage of electricity for heating factors are found to be meaningful. The factor that effect the use of electricity is found to be family member size. Increase of the electrical consumption related to the increase of income can be explained by the change of user habits. For example, more than one TV, use of computers, use of devices that can be addressed to higher income such as clothes dryer. Increase of the electrical consumption related to the increase of residence size can be explained by the increase of electrical device tally. The most obvious example is the increased tally of illumination devices. As the increase of the family member size increases the tally of the electrical devices, it increases the electrical consumption. High rate of use of electricity for heating also increase the electrical consumption as expected. In the scope of this study, in addition to validation of this phenomenon, increase rate is also obtained. However, electricity bill per people is more important than the average residential electric invoice cost. Prediction of the electrical consumption per people is of help for the prediction of the future electricity demand at macro scale. For example, even if the population stays constant, increase of the electricity demand may be an indication of the changes that individuals make in their household life, and this situation contains a hidden unknown in terms of current demand predictions. As the number of people who are away from family life, and who live alone increases, encountering unexpected electricity demand will be obvious. Thus, a careful examination of the family life, which is a field of sociology, and prediction of the changes that may occur until 2025 is crucial. Also, the desire for living in a larger residence, with the increase in income will bring an increase of demand for more electricity. Younger generation has less environmental consciousness than older generation in terms of electrical consumption. For this, it may be useful to concentrate on educations about electrical consumption and environmental consciousness at high schools and college. Also, competitions between the younger ones can be arranged to increase environmental consciousness. With the help of social networks, which is widely used by younger generation, environmental consciousness can be increased. Governmental incentive for

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