Electricity Consumption Forecast Model Using Household Income: Case Study in Tanzania

Abstract

When considering the electrification of a particular region in developing country, the electricity consumption in that region must be estimated. In sub-Saharan Africa, which is one of the areas with the lowest electrification rates in the world, the villages of minority groups are scattered over a vast area of land, so electrification using distributed generators is being actively studied. Specifically, constructing a microgrid or introducing a solar system to each household is being considered. In this case, the electricity consumption of each area needs to be estimated, then a system with enough capacity could be introduced. In this study, we propose a household income electricity consumption model to estimate the electricity consumption of a specific area. We first estimate the electricity consumption of each household based on income and the electricity consumption of a specific area can be derived by adding up them in that area. Through a case study in Tanzania, electricity consumption derived using this model was compared with electricity consumption published by TANESCO, and the validity of the model was verified. We forecasted the electricity consumption in each region using the household income electricity consumption model, and the average forecast accuracy was 74%. The accuracy was 87% when the electricity consumption in Tanzania mainland was forecasted by adding the predicted values.

The effect of electricity consumption determinants in household load forecasting models

“…This result indicates that a load forecasting model can achieve better results with income variables rather than with house size and number of electric appliances in the context of using only one determinant. This finding agrees with the research results reported by [12,15].…”

“…Concerning results obtained when the three predictors were used, it is obvious that forecasting accuracy in household consumption can be optimized with the use of more than one variable. The finding in this work coincides with the claim advocated by [15,21,22] in which they recommend that future work should consider the use of more predictor variables to improve the accuracy. The prediction performance in all four cases described above is shown using bar charts in Figs.…”

“…This invisibility makes it difficult to establish a robust forecasting model. A study to investigate the determinant that has a great effect on forecasting accuracy has been conducted [15]. In such a study, the authors confirm that the use of the "income" variable leads to attaining better accuracy than other factors.…”

“…In such a study, the authors confirm that the use of the "income" variable leads to attaining better accuracy than other factors. Furthermore, the authors recommend that the use of more than one predictor variable, such as house size and others, would increase the forecasting accuracy of the model [15]. To the best of our knowledge, there is no research study on Tanzania's context, attempted to confirm the claim stated by the authors in [15].…”

“…Furthermore, the authors recommend that the use of more than one predictor variable, such as house size and others, would increase the forecasting accuracy of the model [15]. To the best of our knowledge, there is no research study on Tanzania's context, attempted to confirm the claim stated by the authors in [15]. Therefore, the motivation of this research study is to analyze the effect of the number of predictor variables on household load forecasting performance based on Tanzania's data.…”