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Authors: Lan Lin 1 ; Aldo Dagnino 1 ; Derek Doran 2 and Swapna Gokhale 3

Affiliations: 1 ABB Corporate Research, United States ; 2 Wright State University, United States ; 3 University of Connecticut, United States

ISBN: 978-989-758-048-2

Keyword(s): Machine Learning, Storm Damage Projection, Smart Grid, Data Analytics, On-line Social Media.

Related Ontology Subjects/Areas/Topics: Artificial Intelligence ; Business Analytics ; Computational Intelligence ; Data Analytics ; Data Engineering ; Evolutionary Computing ; Knowledge Discovery and Information Retrieval ; Knowledge-Based Systems ; Machine Learning ; Mining Text and Semi-Structured Data ; Pre-Processing and Post-Processing for Data Mining ; Soft Computing ; Symbolic Systems

Abstract: 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 analy tics approach that uses real-world historical data to help utilities in storm damage projection. Preliminary results from the analysis are also included. (More)

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Paper citation in several formats:
Lin, L.; Lin, L.; Dagnino, A.; Doran, D.; Doran, D. and Gokhale, S. (2014). Data Analytics for Power Utility Storm Planning.In Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014) ISBN 978-989-758-048-2, pages 308-314. DOI: 10.5220/0005128203080314

@conference{kdir14,
author={Lan Lin. and Lan Lin. and Aldo Dagnino. and Derek Doran. and Derek Doran. and Swapna Gokhale.},
title={Data Analytics for Power Utility Storm Planning},
booktitle={Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)},
year={2014},
pages={308-314},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0005128203080314},
isbn={978-989-758-048-2},
}

TY - CONF

JO - Proceedings of the International Conference on Knowledge Discovery and Information Retrieval - Volume 1: KDIR, (IC3K 2014)
TI - Data Analytics for Power Utility Storm Planning
SN - 978-989-758-048-2
AU - Lin, L.
AU - Lin, L.
AU - Dagnino, A.
AU - Doran, D.
AU - Doran, D.
AU - Gokhale, S.
PY - 2014
SP - 308
EP - 314
DO - 10.5220/0005128203080314

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