Student Highlights: Juan P. Montoya-Rincon published a paper in the Institute of Electrical and Electronics Engineers (IEEE) on, “On the Use of Satellite Nightlights for Power Outages Prediction”
JUAN P. MONTOYA-RINCON received the B.S. degree in Mechanical Engineering from EAFIT University, Medellin, Colombia, in 2018. He is currently pursuing the Ph.D. degree in mechanical engineering at the City College of New York, NY, USA. His research interest includes the extreme weather-related power outages prediction using statistical models, power grid resiliency analysis, and hurricane-induced failure in the power transmission systems.
In the beginning of 2022, Juan published the scientific article, “On the Use of Satellite Nightlights for Power Outages Prediction” in IEEE. This article was written alongside co-authors Shams Azad, Tandon School of Engineering, Rabindra Pokhrel, Kathmandu University, Masoud Ghandehari, Tandon School of Engineering, Michael P. Jensen, Brookhaven National Laboratory, and Jorge E. Gonzalez, CUNY The City College of New York.
Brief Synopsis of “On the Use of Satellite Nightlights for Power Outages Prediction” by Juan P. Montoya-Rincon, Shams Azad, Rabindra Porkhrel, Masoud Ghandehari, Michael P. Jensen, and Jorge E. Gonzalez.
Hurricanes cause serious impact to the electrical power infrastructure in the Caribbean. After one month since Hurricane Maria, approximately 80% of the population was still without power. Due to the destruction of the hurricane, electrical power restoration progressed very slowly. Preparation for such events can likely lead to a more effective recovery process. Preparedness can be improved by anticipating the likely location and timing of the storm-induced damage to the power grid. Power Outage (PO) forecast models help identify the vulnerable places before the Hurricane landfall. PO forecast models are reliant on historical power outrage records, however, the records are difficult to acquire because many power utility companies may not record them. In the study completed by Juan and his colleagues, they utilize a satellite-based Visible Infrared Imaging Radiometer Suite (VIIRS) night light data product to predict hurricane –induced PO in areas that have little to no historical PO data records. The processed satellite data was used with geographic variables, and simulated weather data to formulate machine learning-based algorithms to predict PO for future hurricane events. The models were built on simulated data from Hurricane Irma and Hurricane Maria.
Reference:
Montoya-Rincon, J. P., Azad, S., Pokhrel, R., Ghandehari, M., Jensen, M. P., & Gonzalez, J. E. (2022). On the Use of Satellite Nightlights for Power Outages Prediction. IEEE Access, 10, 16729-16739.
