Putting the Smarts in Cities, Buildings, and Electric Grids

A technical brief on our IP “Predictive building management system for improved energy efficiency in smart buildings”  is now online and is available for licensing
If you are interested to sponsor research on a Smart-X topic,  email agb6 at columbia dot edu
Reports produced by our group availble at Columbia’s Academic Commons



The Smart-X Group is within the Center for Computational Learning Systems at Columbia University and is part of the Data Science Institute.  Albert Boulanger leads the group.

Our group had a start with Roger Anderson’s and Albert Boulanger’ s work on Computer Aided Lean Management (CALM) concepts developed for the Energy Industry, summarized in this 2007 tutorial.  This lead to a book  Computer-Aided Lean Management for the Energy Industry authored by Roger N. Anderson , Albert Boulanger, John A. Johnson, and Arthur Kressner (out of print, but available digitally on Google Play).


We realized, in looking at the nature of the Energy Industry where many of the assets are in the “last mile”, that completing the arch in decision support systems entails learning how to go from problems to solutions over a time horizon which indicated approximate dynamic programming or reinforcement learning  type of algorithms were appropriate. One concept we developed early-on in this vein was ThreatSim, described in this Smart Grid White Paper  The idea is to use approximate dynamic programming  to learn policies that mitigate threats to systems. Threats could be cyberattacks or climate change threats, etc.

This led to several projects with Consolidated Edison using machine learning and statistics to study failures on their distribution system and to make failure susceptibility predictions actionable with several decision support applications. This work is summarized in the paper Machine Learning for the New York City Power Grid

slide6We were a thought leader in a Dept. of Energy funded Con Edison-led Smart Grid project. Our research for this project was to apply Dynamic Treatment Regimes to formulate optimized repair policies of power distribution components and other research entailed using Approximate Dynamic Programming for optimizing load curtailment decisions in distribution networks.

From our Con Edison work, we branched out to other Smart Cities and Smart Buildings projects:


Finally our work with Con Edison continues. We are doing a cost vs. benefit analysis using causal inference of Con Edison programs concerning their secondary distribution networks and developing image analysis tools for thermal imaging inspections of underground structures for Con Edison.

Contact agb6 at columbia dot edu.