Predicting Spatiotemporal Impacts of Weather on Power Systems Using Big Data Science

  • Mladen KezunovicEmail author
  • Zoran Obradovic
  • Tatjana Dokic
  • Bei Zhang
  • Jelena Stojanovic
  • Payman Dehghanian
  • Po-Chen Chen
Part of the Studies in Big Data book series (SBD, volume 24)


Due to the increase in extreme weather conditions and aging infrastructure deterioration, the number and frequency of electricity network outages is dramatically escalating, mainly due to the high level of exposure of the network components to weather elements. Combined, 75% of power outages are either directly caused by weather-inflicted faults (e.g., lightning, wind impact), or indirectly by equipment failures due to wear and tear combined with weather exposure (e.g. prolonged overheating). In addition, penetration of renewables in electric power systems is on the rise. The country’s solar capacity is estimated to double by the end of 2016. Renewables significant dependence on the weather conditions has resulted in their highly variable and intermittent nature. In order to develop automated approaches for evaluating weather impacts on electric power system, a comprehensive analysis of large amount of data needs to be performed. The problem addressed in this chapter is how such Big Data can be integrated, spatio-temporally correlated, and analyzed in real-time, in order to improve capabilities of modern electricity network in dealing with weather caused emergencies.


Aging infrastructure Asset management Big data Data analytics Data mining Insulation coordination Outage management Power system Solar generation forecast Weather impact 


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Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Mladen Kezunovic
    • 1
    Email author
  • Zoran Obradovic
    • 2
  • Tatjana Dokic
    • 1
  • Bei Zhang
    • 1
  • Jelena Stojanovic
    • 2
  • Payman Dehghanian
    • 1
  • Po-Chen Chen
    • 1
  1. 1.Department of Electrical and Computer EngineeringTexas A&M UniversityCollege StationUSA
  2. 2.Computer and Information DepartmentTemple UniversityPhiladelphiaUSA

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