A Bottom-Up Weather-Sensitive Residential Demand Model for Developing Countries
We develop a novel method method to estimate unsuppressed demand for developing countries. A bottom-up approach is employed using socioeconomic data and a time-of-use database developed from a householder survey. This is used to simulate household activity profiles that are converted into electrical load time series by simulating electrical appliance use. Reanalysis weather data is used in the simulation of ambient conditions for the generation of cooling demand profiles. The time series model is validated against results of a small-scale residential metering trial and is shown to be a credible research tool for electrical demand studies in developing countries that have power networks constrained by intermittent load management program. An illustrative analysis presents regional and national peak load estimates for a range of appliance ownership scenarios that demonstrates the value added by the model.
Presidential Special Scholarship for Innovation and Development (PRESSID)