Improving emergency storm planning using machine learning
Extreme weather events pose significant challenges to power utilities as they require very rapid decision making regarding expected storm impact and necessary storm response efforts. In recent years National Grid has responded to a large number of events in its Massachusetts service territory includ...
Main Authors: | Angalakudati, Mallikarjun, Calzada, Jorge, Gonynor, Jonathan, Raad, Nicolas, Schein, Jeremy, Warren, Cheryl, Williams, John, Papush, Anna Michelle, Monsch, Matthieu Frederic, Farias, Vivek F., Perakis, Georgia, Whipple, Sean David |
---|---|
Other Authors: | Massachusetts Institute of Technology. Institute for Data, Systems, and Society |
Format: | Article |
Language: | en_US |
Published: |
Institute of Electrical and Electronics Engineers (IEEE)
2018
|
Online Access: | http://hdl.handle.net/1721.1/116226 https://orcid.org/0000-0001-7400-9209 https://orcid.org/0000-0002-5856-9246 https://orcid.org/0000-0002-0888-9030 |
Similar Items
-
Business Analytics for Flexible Resource Allocation Under Random Emergencies
by: Angalakudati, Mallik, et al.
Published: (2015) -
A Data-Driven Approach to Personalized Bundle Pricing and Recommendation
by: Papush, Anna Michelle, et al.
Published: (2021) -
Predictive storm damage modeling and optimizing crew response to improve storm response operations
by: Whipple, Sean David
Published: (2014) -
Large scale prediction models and algorithms
by: Monsch, Matthieu (Matthieu Frederic)
Published: (2014) -
Promotion Optimization for Multiple Items in Supermarkets
by: Kalas, Jeremy J., et al.
Published: (2021)