Forecasting satisfaction of grid electricity to a rural household: examples from Nepal

In this paper, the energy poverty of 300 households of national grid electricity users is quantified and analyzed. Multivariate statistics is used in this process. These households only have a partial access to electricity. Canonical correlation analysis (CCA) and structural equation modeling (SEM) are used in forecasting consumer satisfaction and thus assessing energy poverty. A multivariate data from a sample survey of 300 households are used here. Structural relationships between distance covered for firewood, time spent in firewood collection, collection of firewood (who), electricity satisfaction, residents 15 years and more of age, size of females in a family, size of family, payment of electricity bill, registration of electricity connection, decisions regarding electricity, profit kept from electricity connection and delivery at home or hospital are quantified and analyzed here. Here time spent in firewood collection is predicted with SEM. Among several structural models specified, two most suitable time spent structural models are discussed in detail. Then one model is chosen as final; model specification, identification, estimation, testing, identification and validation procedures are used. CCA is used in predicting relationship between these variables classified into two groups cause and effect (of energy poverty). Latent factors playing a critical role are identified and measured. Such studies are very crucial for countries like Nepal with limited and scarce official data.