A Model Predicting Electrical Energy Management Practices among Residents in Niger State, Nigeria

Abstract

The study predicted electrical energy management practices among residents, using regression model. Two research questions and a hypothesis were formulated to guide the study. Three variables used were (Income levels, Age groups and Educational levels) of electrical energy users. The study adopted a cross sectional survey research design. The population of the study was made up of 191,416 heads of households in residential buildings connected to the distribution network in 25 Local Governments of Niger State. The sample for the study consisted of 1,290 heads of households drawn through multistage sampling techniques. Out of 1,290 numbers of questionnaires administered to residents, 987 were returned representing 76.5% return rate. The instrument used for data collection was a structured questionnaire. Multiple Mean and standard deviation (SD) were used to analyzed research questions. Correlation and multiple regression analysis were used to analyzed hypothesis which determined the no relationship at (P ˂ .05) level of significance on the practices of residents on electrical energy management in residential buildings in Niger State and to show the variable that has effect and the degree of the effect on the electrical energy management practices. The model developed is this shows that person income level, age group and educational level were significant in predicting practices on electrical energy management with significance values of 0.02, 0.00 and 0.00 less than the of P ˂ .05. Furthermore, age groups have a negative impact on electrical energy management practices in contrast to the income levels and educational qualifications. This is indicated by the values of their coefficient of +0.07, -0.062 and 0.097 respectively. The model significantly predicts electrical energy management practices. It is strengthen by the significant value of 0.00 less than the of P ˂ 0.05. The public awareness should be specifically designed for electricity users at different levels of age, education and income group. As these sociodemographic variables predicts electricity energy usage.