|Title||Data Analytics for Power Utility Storm Planning|
|Publication Type||Conference Proceedings|
|Year of Publication||2014|
|Authors||Lan Lin, Aldo Dagnino, Derek Doran, Swapna Gokhale|
|Conference Name||6th International Conference on Knowledge Discovery and Information Retrieval|
|Conference Location||Rome, Italy|
|Keywords||Data Analytics, machine learning, On-line social media, Smart grid, Storm damage projection|
As the world population grows, recent climatic changes seem to bring powerful storms to populated areas. The impact of these storms on utility services is devastating. Hurricane Sandy is a recent example of the enormous damages that storms can inflict on infrastructure, society, and the economy. Quick response to these emergencies represents a big challenge to electric power utilities. Traditionally utilities develop preparedness plans for storm emergency situations based on the experience of utility experts and with limited use of historical data. With the advent of the Smart Grid, utilities are incorporating automation and sensing technologies in their grids and operation systems. This greatly increases the amount of data collected during normal and storm conditions. These data, when complemented with data from weather stations, storm forecasting systems, and online social media, can be used in analyses for enhancing storm preparedness for utilities. This paper presents a data analytics approach that uses real-world historical data to help utilities in storm damage projection. Preliminary results from the analysis are also included.