Title: On the Use of Satellite Nightlights for Power Outages Prediction

Journal Article · · IEEE Access
ORCiD logo [1]; ORCiD logo [2];  [3];  [2]; ORCiD logo [4]; ORCiD logo [5]
  1. City College of New York, NY (United States)
  2. New York Univ. (NYU), NY (United States)
  3. Kathmandu Univ., Dhulikhel (Nepal)
  4. Brookhaven National Lab. (BNL), Upton, NY (United States)
  5. City College of New York, NY (United States); Brookhaven National Lab. (BNL), Upton, NY (United States)
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Hurricanes are a dominant disaster in the Caribbean, always causing serious power outages throughout the islands. Hurricane Maria was a prime example, causing unimaginable destruction of the power infrastructure of Puerto Rico (PR). Consequently, one month after the hurricane landfall, approximately 80% of the population was still without power. After an event of such massive destruction, the electric power restoration process progresses very slowly. This timeline can be improved using power outage (PO) forecast models that help identify the vulnerable places before the hurricane landfall. Generally, these models are trained with historical power outages records, associated data on weather conditions, and additional information about the natural and built environments. However, PO records are often difficult to acquire, and, in many instances, the power utility companies may not record them. This study utilizes a satellite-based Visible Infrared Imaging Radiometer Suite (VIIRS) night light data product as a surrogate for the power delivery to predict hurricane-induced PO in areas having limited to nonexistent historical data records. The processed satellite data is then used along with geographic variables, and simulated weather data to formulate machine learning-based algorithms to predict PO for future hurricane events. These models are applied and validated in the context of the PR catastrophic storm, Hurricane Maria.

Research Organization:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Organization:
National Science Foundation (NSF); USDOE; USDOE Laboratory Directed Research and Development (LDRD) Program
Grant/Contract Number:
SC0012704
OSTI ID:
1846366
Report Number(s):
BNL-222753-2022-JAAM
Journal Information:
IEEE Access, Journal Name: IEEE Access Vol. 10; ISSN 2169-3536
Publisher:
IEEECopyright Statement
Country of Publication:
United States
Language:
English

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54 ENVIRONMENTAL SCIENCES
data models
extreme events
hurricanes
machine learning
night-time light
power outage prediction
power system reliability
predictive models
spatial resolution
storms
weather simulation
wind speed