Modelling spatio-temporal energy consumption from nighttime radiance satellite dataset

Abstract

National electricity consumption increases in line with continuous population growth and other socio-economic factors. The national electric power capacity goal develops largely for industrial manufacture and new settlement. The electrification- ratio on the target; is based on the accessibility of electricity services. The spatial distribution of electricity services coverage over the Indonesian territory is insufficient, particularly over the remote area that is out of electric services. Modeling by spatial (location) and temporal (year) to estimate electricity or energy consumption is necessary to develop using a low-light nighttime satellite dataset, therefore spatial boundaries can be accomplished. The modeling procedure starts by preparing the data frame of the independent variable input (amount of radiance) and the dependent variable output (the consumption of electricity or energy). The modelling method uses the curve-fitting approach where the indicator results by evaluating the R-square and RMSE values. The output model function is used to convert radiances into electrical power consumption units with a certain degree of accuracy. The selection of the input-output variable was achieved after variable analysis with the highest R-square outcome. Results indicate that the model functions in a polynomial form and correlations between variables are not simple. The selection of various model functions did not change the degree of correlation. The accumulative of energy radiances as independent variable input provides the optimum correlation result. The energy consumption from street lighting, in general, offers appropriate information that can be seen from satellites. The model function can be applied to a narrower spatial scale by input variable constraints.