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DR-Advisor

Data-Driven Demand Response Recommender System




Cleantech prize

Alegheny Cleantech University Prize Winners!


DR-Advisor is the winner of the $50,000 top prize at the 2016 Alegheny Cleantech University Prize.


See the Department of Energy's announcement here.

FEATURES


    • DR BASELINE PREDICTION

      Fast time scale prediction of the baseline power consumption of the building.

    • DR STRATEGY EVALUATION

      Optimally choose the best DR strategy from a set of pre-determined strategies during a DR event.

    • DR STRATEGY SYNTHESIS

      Reccommendations of optimal control actions to be taken to meet the required DR curtailment based on building operation and weather forecasts

    App Features
    • NO ADDITIONAL SENSORS

      DR-Advisor does not require you to invest in retrofitting the building with 100s of sesnors. All we require is the building's location and historical power consumption.

    • HIGHLY INTERPRETABLE SOLUTIONS

      Our regression trees based methods are very easy to understand and highly interpretable offering a completley human centric solution.

    • INSIGHTFUL DATA ANALYTICS

      Our data-driven models provide insight about how the building is consuming power and can classify irregular building operation.

    App Features

SCREENSHOTS


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CASE STUDY


The performance of DR-Advisor was evaluated using data from a large 12 story office building. The building consists of 73 zones with a total area of 500,000 sq ft. There are 2,397 people in the building during peak occupancy.

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DR-Advisor will be released soon !


GET IN TOUCH

Inquiries about this product and release

Email the Team


TEAM


Meet the team behind DR-Advisor


Madhur Behl

Suneet Sharma

Madhur Behl

Francesco Smarra

Madhur Behl

Santiago Gonzalez

Collaboration


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Publications

Behl, M., Jain, A. and Mangharam, R.
Data-Driven Modeling, Control and Tools for Cyber-Physical energy Systems
International Conference on Cyber-Physical Systems (ICCPS), ACM/IEEE, 2016

Behl, M. and Mangharam, R.
A Data-Driven Demand Response Recommender System
Journal of Applied Energy, 2016

Behl, M. and Mangharam, R.
Sometimes, Money Does Grow on Trees: DR-Advisor, A Data Driven Demand Response Recommender System
Semiconductor Research Corporation (SRC) TECHCON, SRC, 2015, Vol. Publication ID:P084437

Behl, M. and Mangharam, R.
Sometimes, Money Does Grow On Trees: Real-Time Demand Response With DR-Advisor
2nd ACM International Conference on Embedded Systems For Energy-Efficient Built Environments (BuildSys), Seoul, South Korea, ACM, 2015, pp. 137-146

Behl, M., Nghiem, T. and Mangharam, R.
IMpACT Inverse Model Accuracy and Control Performance Toolbox for Buildings
2014 IEEE International Conference on Automation Science and Engineering (CASE), IEEE, 2014, pp. 1109-1114

Behl, M., Nghiem, T. and Mangharam, R.
Model-IQ: Uncertainty Propagation from Sensing to Modeling and Control in Buildings.
ACM/IEEE 5th International Conference on Cyber-Physical Systems, ACM/IEEE, 2014, pp. 13-24

Behl, M., Shah, N. D., Vadakedathu, L., Wheeler, D. and Mangharam, R.
Demo Abstract: EnergyLab: Building Energy Testbed for Demand-response
2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)
ACM/IEEE, 2013, pp. 303-304

Behl, M., Nghiem, T. and Mangharam, R.
Green Scheduling for Energy-Efficient Operation of Multiple Chiller Plants
IEEE 33rd Real-Time Systems Symposium (RTSS 2012)
IEEE, 2012, pp. 195 - 204

Bernal, W., Behl, M., Nghiem, T. and Mangharam, R.
MLE+: a tool for integrated design and deployment of energy efficient building controls
BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings
ACM New York, NY, USA ©2012, 2012, pp. 123-130

Behl, M., Aneja, M., Jain, H. and Mangharam, R.
EnRoute: An energy router for energy-efficient buildings
10th International Conference on Information Processing in Sensor Networks (IPSN), 2011
IEEE, 2011, pp. 125 - 126