A team of students led by researchers in the Energy Access Project will develop means to evaluate non-technical electricity losses (theft) in developing countries through machine learning techniques applied to smart meter electricity consumption data. Students will use data from smart meters installed at transformers and households through a randomized control trial. Students will develop algorithms that can be used to detect anomalies in the electricity consumption data and create a dataset of such indicators. This project will provide researchers with new ways of incorporating electricity consumption data and applications for electricity utilities in developing country settings.
Faculty Lead: Robyn Meeks