Dissertation

Madhur Behl (2015) Ph.D. Thesis Electrical & Systems Engineering "Data-driven modeling, control and tools for cyber-physical energy systems", Univeristy of Pennsylvania. Available from Dissertations & Theses @ University of Pennsylvania; ProQuest Dissertations &Theses Global. (1761166227) pp. 138.

BibTeX:
                @misc{ 
                author={Behl,Madhur},
                year={2015},
                title={Data-driven modeling, control and tools for cyber-physical energy systems},
                journal={ProQuest Dissertations and Theses},
                pages={138},
                note={Copyright - Copyright ProQuest, UMI Dissertations Publishing 2015; Last updated - 2016-02-01},
                keywords={Applied sciences; Control; Cyber-physical systems; Data-driven; Demand response; Energy; Regression tree; Electrical engineering; Computer science; 0984:Computer science; 0791:Energy; 0544:Electrical engineering},
                isbn={9781339429656},
                language={English},
                url={http://search.proquest.com/docview/1761166227?accountid=14707},
              }
            }
          
Abstract: Energy systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled systems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles based models, at all scales and levels. Furthermore, peak power reduction programs like demand response (DR) are becoming increasingly important as the volatility on the grid continues to increase due to regulation, integration of renewables and extreme weather conditions. In order to shield themselves from the risk of price volatility, end-user electricity consumers must monitor electricity prices and be flexible in the ways they choose to use electricity. This requires the use of control-oriented predictive models of an energy system’s dynamics and energy consumption. Such models are needed for understanding and improving the overall energy efficiency and operating costs. However, learning dynamical models using grey/white box approaches is very cost and time prohibitive since it often requires significant financial investments in retrofitting the system with several sensors and hiring domain experts for building the model. We present the use of data-driven methods for making model capture easy and efficient for cyber-physical energy systems. We develop Model-IQ, a methodology for analysis of uncertainty propagation for building inverse modeling and controls. Given a grey-box model structure and real input data from a temporary set of sensors, Model-IQ evaluates the effect of the uncertainty propagation from sensor data to model accuracy and to closed-loop control performance. We also developed a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate data collection for model training and control. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy. We also present DR-Advisor, a data-driven demand response recommender system for the building's facilities manager which provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. We develop a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based demand response methods for a large DoE commercial reference building and leads to a significant amount of load curtailment (of 380kW) and over $45,000 in savings which is 37.9% of the summer energy bill for the building. The performance of DR-Advisor is also evaluated for 8 buildings on Penn's campus; where it achieves 92.8% to 98.9% prediction accuracy. We also compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE's benchmarking data-set for energy prediction.

Book Chapters

Madhur Behl and Rahul Mangharam (2017), "Chapter 9: Data-Driven Modeling, Control, and Tools for Smart Cities", Pg 243-272, Book - Smart Cities: Foundations and Principles, John Wiley & Sons Inc, ISBN: 978-1-119-22639-0, June 2017

Journal Papers

[2] Achin Jain, Rahul Mangharam, and Madhur Behl (2017), "Data Predictive Control for Cyber-Physical Energy Systems.", ACM Transactions on Cyber-Physical Systems. [Under Review]
BibTeX:
              @article{DPCbehl2016,
              author = {Achin Jain, Rahul Mangharam, and Madhur Behl},
              title = {Data Predictive Control for Cyber-Physical Energy Systems.},
              journal = {ACM Transactions on Cyber-Physical Systems.},
              year = {2017},
              note = {[Under Review]}
            }
          
Abstract: Decisions on how best to optimize today’s energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive. Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. We consider the problem of data-driven end-user demand response and peak power reduction for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies, synthesizing DR control actions, and reducing the peak power consumption. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of 380kW and over $45,000 in DR revenue. A data predictive control with regression trees (DPCRT) algorithm, is also presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves 92.8% to 98.9% prediction accuracy for 8 buildings on Penn’s campus. We compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE’s benchmarking data-set for energy prediction.
[1] Madhur Behl and Francesco Smarra and Rahul Mangharam (2016), "A Data-Driven Demand Response Recommender System", Journal of Applied Energy.
BibTeX:
              @article{BehlMangharam2015,
              author = {Madhur Behl and  Francesco Smarra and Rahul Mangharam},
              title = {A Data-Driven Demand Response Recommender System},
              journal = {Journal of Applied Energy},
              year = {2016},
              url = {http://www.sciencedirect.com/science/article/pii/S030626191630246X}
            }
          
Abstract: Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR ap- proaches are predominantly completely manual and rule-based or involve deriving first principles based models which are ex- tremely cost and time prohibitive to build. We consider the problem of data-driven end-user DR for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies and synthesizing DR control actions. The challenge is in evaluating and taking control decisions at fast time scales in order to curtail the power consumption of the building, in return for a financial reward. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of 380kW and over $45, 000 in savings. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves 92.8% to 98.9% prediction accuracy for 8 buildings on Penn’s campus. We compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE’s benchmarking data-set for energy prediction

Peer Reviewed Conference Papers

[17] Achin Jain, Madhur Behl, and Rahul Mangharam (2017), "Data Predictive Control for building energy management.", Proceedings of the 2017 American Control Conference (ACC), Seattle, WA, USA. [Best Energy Systems Paper Award]
BibTeX:
              @conference{DpcACC17,
              author = {Achin Jain and Madhur Behl and Rahul Mangharam},
              title = {Data Predictive Control for building energy management},
              journal = {Proceedings of the American Control Conference},
              year = {2017},
              note = {[Best Energy Systems Paper Award]},
              url = {http://repository.upenn.edu/cgiviewcontent.cgi?article=1115&context=mlab_papers}
            }
          
Abstract: Decisions on how to best optimize energy systems operations are becoming ever so complex and conflicting, that model-based predictive control (MPC) algorithms must play an important role. However, a key factor prohibiting the widespread adoption of MPC in buildings, is the cost, time, and effort associated with learning first-principles based dynamical models of the underlying physical system. This paper introduces an alternative approach for implementing finite-time receding horizon control using control-oriented data-driven models. We call this approach Data Predictive Control (DPC). Specifically, by utilizing separation of variables, two novel algorithms for implementing DPC using a single regression tree and with regression trees ensembles (random forest) are presented. The data predictive controller enables the building operator to trade off energy consumption against thermal comfort without having to learn white/grey box models of the systems dynamics. We present a comprehensive numerical study which compares the performance of DPC with an MPC based energy management strategy, using a single zone building model. Our results demonstrate that performance of DPC is comparable to an MPC controller, with only 3.8% additional cost in terms of optimal objective function and within 95% in terms of R2 score, thereby making it an alluring alternative to MPC, whenever the associated cost of learning the model is high.
[16] Achin Jain, Rahul Mangharam, and Madhur Behl (2016), "Data Predictive Control for Peak Power Reduction.", 3rd ACM International Conference on Embedded Systems For Energy-Efficient Built Environments (BuildSys), Stanford, CA, USA. [Best Presentation Award]
BibTeX:
              @inproceedings{Jain:2016:DPC:2993422.2993582,
 				author = {Jain, Achin and Mangharam, Rahul and Behl, Madhur},
 				title = {Data Predictive Control for Peak Power Reduction},
 				booktitle = {Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments},
 				series = {BuildSys '16},
 				year = {2016},
 				isbn = {978-1-4503-4264-3},
 				location = {Palo Alto, CA, USA},
 				pages = {109--118},
 				numpages = {10},
 				url = {http://doi.acm.org/10.1145/2993422.2993582},
 				doi = {10.1145/2993422.2993582},
 				acmid = {2993582},
 				publisher = {ACM},
 				address = {New York, NY, USA},
 				keywords = {Building control, Machine learning, Peak power reduction, Predictive control},
				} 
          
Abstract: Decisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs.
[15] Rahul Mangharam, Houssam Abbas, Madhur Behl, Kuk Jang, Miroslav Pajic, Zhihao Jiang (2016), "Three challenges in cyber-physical systems", 8th International Conference on Communication Systems and Networks (COMSNETS), 2016 Vienna, Austria
BibTeX:
 			@inproceedings{mangharam2016three,
  				title={Three challenges in cyber-physical systems},
  				author={Mangharam, Rahul and Abbas, Houssam and Behl, Madhur and Jang, Kuk and Pajic, Miroslav and Jiang, Zhihao},
  				booktitle={Communication Systems and Networks (COMSNETS), 2016 8th International Conference on},
  				pages={1--8},
  				year={2016},
  				organization={IEEE}
				}
          
Abstract: The tight coupling of computation, communication and control with physical systems such as actuation of closed-loop medical devices within the human body, peak power minimization by coordination of controllers across large industrial plants, and fast life-critical decision making by autonomous vehicles, present a set of fundamental and unique challenges. Each of these require new approaches at the intersection of multiple scientific, human and systems disciplines. We discuss five such challenges which require creative insights and application of model-based design, control systems, scheduling theory, formal methods, statistical machine learning and domain-specific experimentation. We ask the following questions: (1) An autonomous medical device is implanted to control your heart over a period of 5-7 years. How do you guarantee the software in the device provides safe and effective treatment under all physiological conditions? (2) Electricity prices in the US have summer peaks that are over 32× their average prices and winter peaks that are 86×. How can buildings respond to massive swings in energy prices at fast time scales? (3) While wireless has been successfully used for open-loop monitoring and tracking, how can we operate closed-loop control systems over a network of wireless controllers. Furthermore, how can we ensure robust, optimal and secure control in the presence of node/link failures and topology changes?
[14] Madhur Behl, Achin Jain and Rahul Mangharam (2016), "Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems", International Conference on Cyber-Physical Systems (ICCPS), Vienna, Austria.
BibTeX:
              @conference{behl_iccps16,
              author = {Madhur Behl and Achin Jain and Rahul Mangharam},
              title = {Data-Driven Modeling, Control and Tools for Cyber-Physical energy Systems},
              journal = {International Conference on Cyber-Physical Systems (ICCPS)},
              year = {2016},
              note = {[To Appear]},
              url = {http://repository.upenn.edu/mlab_papers/80/}
            }
          
Abstract: Demand response (DR) is becoming increasingly important as the volatility on the grid continues to increase. Current DR approaches are completely manual and rule-based or involve deriving first principles based models which are extremely cost and time prohibitive to build. We consider the problem of data-driven end-user DR for large buildings which involves predicting the demand response baseline, evaluating fixed rule based DR strategies and synthesizing DR control actions. We provide a model based control with regression trees algorithm (mbCRT), which allows us to perform closed-loop control for DR strategy synthesis for large commercial buildings. Our data-driven control synthesis algorithm outperforms rule-based DR by 17% for a large DoE commercial reference building and leads to a curtailment of 380kW and over $45,000 in savings. Our methods have been integrated into an open source tool called DR-Advisor, which acts as a recommender system for the building's facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining operations and maximizing the economic reward. DR-Advisor achieves 92.8% to 98.9% prediction accuracy for 8 buildings on Penn's campus. We compare DR-Advisor with other data driven methods and rank 2nd on ASHRAE's benchmarking data-set for energy prediction.
[13] Madhur Behl and Rahul Mangharam (2015), "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. , pp. 137-146.
BibTeX:
              @conference{behl_buildsys15,
              author = {Madhur Behl and Rahul Mangharam},
              title = {Sometimes, Money Does Grow On Trees: Real-Time Demand Response With DR-Advisor},
              journal = {2nd ACM International Conference on Embedded Systems For Energy-Efficient Built Environments (BuildSys), Seoul, South Korea},
              year = {2015},
              pages = {137-146},
              url = {http://repository.upenn.edu/mlab_papers/82/},
              doi = {10.1145/2821650.2821664}
            }
          
Abstract: Real-time electricity pricing and demand response has be- come a clean, reliable and cost-effective way of mitigating peak demand on the electricity grid. We consider the problem of end-user demand response (DR) for large commer- cial buildings which involves predicting the demand response baseline, evaluating fixed DR strategies and synthesizing DR control actions for load curtailment in return for a financial reward. Using historical data from the building, we build a family of regression trees and learn data-driven models for predicting the power consumption of the building in real-time. We present a method called DR-Advisor called DR- Advisor, which acts as a recommender system for the build- ing’s facilities manager and provides suitable control actions to meet the desired load curtailment while maintaining op- erations and maximizing the economic reward. We evaluate the performance of DR-Advisor for demand response using data from a real office building and a virtual test-bed.
[12] Madhur Behl and Rahul Mangharam (2015), "Sometimes, Money Does Grow on Trees: DR-Advisor, A Data Driven Demand Response Recommender System", Semiconductor Research Corporation (SRC) TECHCON. Vol. Publication ID:P084437, Austin, USA. [Best Paper Award]
BibTeX:
              @conference{behl_techcon,
              author = {Madhur Behl and Rahul Mangharam},
              title = {Sometimes, Money Does Grow on Trees: DR-Advisor, A Data Driven Demand Response Recommender System},
              journal = {Semiconductor Research Corporation (SRC) TECHCON},
              year = {2015},
              volume = {Publication ID:P084437},
              note = {Best Paper Award in the Internet of Things Session},
              url = {http://repository.upenn.edu/mlab_papers/81/}
            }
          
Abstract: Unprecedented amounts of information from mil- lions of smart meters and thermostats installed in recent years has left the door open for better understanding, analyzing and using the insights that data can provide, about the power consumption patterns of a building. The challenge with using data-driven approaches, is to close the loop for near real-time control and decision making in large buildings. Furthermore, providing a technological solution alone is not enough, the solution must also be human centric. We consider the problem of end-user demand response for commercial buildings. Using historical data from the building, we build a family of regression trees based models for predicting the power consumption of the building in real-time. We have built DR-Advisor, a recommender system for the building’s facilities manager, which provides optimal control actions to meet the required load curtailment while maintaining building operations and maximizing the economic reward.
[11] Madhur Behl, Truong Nghiem and Rahul Mangharam (2015), "DR-Advisor: A Data Driven Demand Response Recommender System", Proceedings of International Conference CISBAT: Future Buildings and Districts Sustainability from Nano to Urban Scale. Vol. EPFL-CONF-213354, pp. 401-406. Lausane, Switzerland.
BibTeX:
              @proceedings{behl_cisbat,
              author = {Madhur Behl and Truong Nghiem and Rahul Mangharam},
              title = {DR-Advisor: A Data Driven Demand Response Recommender System},
              journal = {Proceedings of International Conference CISBAT: Future Buildings and Districts Sustainability from Nano to Urban Scale},
              year = {2015},
              volume = {EPFL-CONF-213354},
              pages = {401-406},
              url = {http://repository.upenn.edu/mlab_papers/78/}
            }
          
Abstract: A data-driven method for demand response baselining and strategy evaluation is pre- sented. Using meter and weather data along with set-point schedule information, we use an ensemble of regression trees to learn non-parametric data-driven models for predicting the power consumption of the building. This model can be used for evaluating demand response strategies in real-time, without having to learn complex models of the building. The methods have been integrated in an open-source tool called DR-Advisor, which acts as a recommender system for the building’s facilities manager by advising on which con- trol actions should be during a demand response event. We provide a case study using data from a large commercial vistural test-bed building to evaluate the performance of the DR-Advisor tool.
[10] Willy Bernal, Madhur Behl, Truong Nghiem and Rahul Mangharam (2015), "Campus-Wide Integrated Building Energy Simulation", 14th International Conference of the International Building Performance Simulation Association. Hyderabad, India.
BibTeX:
              @conference{bernal_ibpsa,
              author = {Willy Bernal and Madhur Behl and Truong Nghiem and Rahul Mangharam},
              title = {Campus-Wide Integrated Building Energy Simulation},
              journal = {14th International Conference of the International Building Performance Simulation Association},
              year = {2015},
              url = {http://repository.upenn.edu/mlab_papers/85/}
            }
          
Abstract: Effective energy management for large campus facilities is becoming increasingly complex as modern heat- ing and cooling systems comprise of several hundred subsystems interconnected to each other. Building energy simulators like EnergyPlus are exceedingly good at modeling a single building equipped with a standalone HVAC equipment. However, the ability to simulate a large campus and to control the dynamics and interactions of the subsystems is limited or missing al- together.
In this paper, we use the Matlab-EnergyPlus MLE+ tool we developed, to extend the capability of EnergyPlus to co-simulate a campus with multiple build- ings connected to a chilled water distribution to a central chiller plant with control systems in Matlab. We present the details of how this simulation can be set- up and implemented using MLE+’s Matlab/Simulink block. We utilize the virtual campus test-bed to evaluate the performance of several demand response strate- gies. We also describe a coordinated demand response scheme which can lead to load curtailment during a demand response event while minimizing thermal discomfort.
[9] Madhur Behl, Truong Nghiem and Rahul Mangharam (2014), "IMpACT Inverse Model Accuracy and Control Performance Toolbox for Buildings", 2014 IEEE International Conference on Automation Science and Engineering (CASE). , pp. 1109-1114. Taipei, Taiwan.
BibTeX:
              @conference{behl_case14,
              author = {Madhur Behl and Truong Nghiem and Rahul Mangharam},
              title = {IMpACT Inverse Model Accuracy and Control Performance Toolbox for Buildings},
              journal = {2014 IEEE International Conference on Automation Science and Engineering (CASE)},
              year = {2014},
              pages = {1109-1114},
              url = {http://repository.upenn.edu/cgi/viewcontent.cgi?article=1083&context=mlab_papers}
            }
          
Abstract: Uncertainty a ects all aspects of building performance: from the identi cation of models, through the implementation of model-based control, to the operation of the deployed systems. Learning models of buildings from sensor data has a fundamental property that the model can only be as accurate and reliable as the data on which it was trained. For small and medium size buildings, a low-cost method for model capture is necessary to take advantage of optimal model-based supervisory control schemes. We present IMpACT, a methodology and a toolbox for analysis of uncertainty propagation for building inverse modeling and controls. Given a plant model and real input data, IMpACT automatically evaluates the e ect of the uncertainty propagation from sensor data to model accuracy and control performance. We also present a statistical method to quantify the bias in the sensor measurement and to determine near optimal sensor placement and density for accurate signal measurements. In our previous work, we considered the end-to-end propagation of uncertainty in the form of xed bias in the sensor data. In this paper, we extend the method to work with random errors in the sensor data, which is more realistic. Using a real building test-bed, we show how performing an uncertainty analysis can reveal trends about inverse model accuracy and control performance, which can be used to make informed decisions about sensor requirements and data accuracy.
[8] Madhur Behl, Truong Nghiem and Rahul Mangharam (2014), "Model-IQ: Uncertainty Propagation from Sensing to Modeling and Control in Buildings.", ACM/IEEE 5th International Conference on Cyber-Physical Systems. Berlin, Germany.
BibTeX:
              @conference{behl_iccps14,
              author = {Madhur Behl and Truong Nghiem and Rahul Mangharam},
              title = {Model-IQ: Uncertainty Propagation from Sensing to Modeling and Control in Buildings.},
              journal = {ACM/IEEE 5th International Conference on Cyber-Physical Systems},
              year = {2014},
              url = {http://repository.upenn.edu/cgi/viewcontent.cgi?article=1082&context=mlab_papers}
            }
          
Abstract: A fundamental problem in the design of closed-loop Cyber-Physical Systems (CPS) is in accurately capturing
the dynamics of the underlying physical system. To provide optimal control for such closed-loop systems,
model-based controls require accurate physical plant models. It is hard to analytically establish (a) how data
quality from sensors affects model accuracy, and consequently, (b) the effect of model accuracy on the
operational cost of model-based controllers. We present the Model-IQ toolbox which, given a plant model
and real input data, automatically evaluates the effect of this uncertainty propagation from sensor data to
model accuracy to controller performance. We apply the Model-IQ uncertainty analysis for model-based
controls in buildings to demonstrate the cost-benefit of adding temporary sensors to capture a building
model. We show how sensor placement and density bias training data. For the real building considered, a bias
of 1% degrades model accuracy by 20%. Model-IQ's automated process lowers the cost of sensor deployment,
model training and evaluation of advanced controls for small and medium sized buildings. Such end-to-end
analysis of uncertainty propagation has the potential to lower the cost for CPS with closed-loop model based
control. We demonstrate this with real building data in the Department of Energy's HUB.
[7] Madhur Behl, Truong Nghiem and Rahul Mangharam (2012), "Green Scheduling for Energy-Efficient Operation of Multiple Chiller Plants", In IEEE 33rd Real-Time Systems Symposium (RTSS 2012). , pp. 195 - 204. San Juan, Puerto Rico.
BibTeX:
              @inproceedings{BehlEtAl12rtss,
              author = {Madhur Behl and Truong Nghiem and Rahul Mangharam},
              title = {Green Scheduling for Energy-Efficient Operation of Multiple Chiller Plants},
              booktitle = {IEEE 33rd Real-Time Systems Symposium (RTSS 2012)},
              year = {2012},
              pages = {195 - 204},
              url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6424803&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6424803},
              doi = {10.1109/RTSS.2012.71}
            }
          
Abstract: In large building systems, such as a university campus, the air-conditioning systems are commonly served by chiller plants, which contribute a large fraction of the total elec- tricity consumption of the campuses. The power consumption of a chiller is highly affected by its Coefficient of Performance (COP), which is optimal when the chiller is operated at or near full load. For a chiller plant, its overall COP can be optimized by utilizing a Thermal Energy Storage (TES) and switching its operation between COP-optimal charging and discharging modes. However, uncoordinated mode switchings of chiller plants may cause temporally-correlated high electricity demand when multiple plants are charging their TES concurrently. In this paper, a Green Scheduling approach, proposed in our previous work, is used to schedule the chiller plants to reduce their peak aggregate power demand while ensuring safe operation of the TES. We present a scheduling algorithm based on backward reach set computation of the TES dynamics. The proposed algorithm is demonstrated in a numerical simulation in Matlab to be effective for reducing the peak power demand and the overall electricity cost.
[6] Truong Nghiem, Madhur Behl, George Pappas and Rahul Mangharam (2012), "Green scheduling for radiant systems in buildings", In IEEE 51st Annual Conference on Decision and Control (CDC). , pp. 7577-7582. IEEE. Maui, Hawaii.
BibTeX:
              @conference{NghiemEtAl12cdc,
              author = {Truong Nghiem and Madhur Behl and George Pappas and Rahul Mangharam},
              title = {Green scheduling for radiant systems in buildings},
              booktitle = {IEEE 51st Annual Conference on Decision and Control (CDC)},
              publisher = {IEEE},
              year = {2012},
              pages = {7577-7582},
              url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6426318&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6426318},
              doi = {10.1109/CDC.2012.6426318}
            }
          
Abstract: In this paper we look at the problem of peak power reduction for buildings with electric radiant floor heating systems. Uncoordinated operation of a multi-zone radiant floor heating system can result in temporally correlated electricity demand surges or peaks in the building's electricity consumption. As peak power prices are 200-400 times that of the nominal rate, this uncoordinated activity can result in high electricity costs and expensive system operation. We have previously presented green scheduling as an approach for reducing the aggregate peak power consumption in buildings while ensuring that indoor thermal comfort is always maintained. This paper extends the theoretical results for general affine dynamical systems and applies them to electric radiant floor heating systems. The potential of the proposed method in reducing the peak power demand is demonstrated for a small-scale system through simulation in EnergyPlus and for a large-scale system through simulation in Matlab.
[5] Willy Bernal, Madhur Behl, Truong Nghiem and Rahul Mangharam (2012), "MLE+: a tool for integrated design and deployment of energy efficient building controls", In BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings. , pp. 123-130. Toronto, Canada.
BibTeX:
              @proceedings{BernalEtAl12buildsys,
              author = {Willy Bernal and Madhur Behl and Truong Nghiem and Rahul Mangharam},
              title = {MLE+: a tool for integrated design and deployment of energy efficient building controls},
              booktitle = {BuildSys '12 Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings},
              publisher = {ACM New York, NY, USA ©2012},
              year = {2012},
              pages = {123-130},
              note = {Best Demo Award},
              url = {http://dl.acm.org/citation.cfm?id=2422553},
              doi = {10.1145/2422531.2422553}
            }
          
Abstract: We present MLE+, a tool for energy-efficient building automation design, co-simulation and analysis. The tool leverages the high-fidelity building simulation capabilities of EnergyPlus and the scientific computation and design capabilities of Matlab for controller design. MLE+ facilitates integrated building simulation and controller formulation with integrated support for system identification, control design, optimization, simulation analysis and communication between software applications and building equipment. It provides streamlined workflows, a graphical front-end, and debugging support to help control engineers eliminate design and programming errors and take informed decisions early in the design stage, leading to fewer iterations in the building automation development cycle. We show through an example and two case studies how MLE+ can be used for designing energy-efficient control algorithms for both simulated buildings in EnergyPlus and real building equipment via BACnet.
[4] Truong Nghiem, Madhur Behl, Rahul Mangharam and George Pappas (2012), "Scalable scheduling of building control systems for peak demand reduction", In American Control Conference (ACC),. , pp. 3050 - 3055. Montreal, Canada.
BibTeX:
              @conference{acc12,
              author = {Truong Nghiem and Madhur Behl and Rahul Mangharam and George Pappas},
              title = {Scalable scheduling of building control systems for peak demand reduction},
              booktitle = {American Control Conference (ACC),},
              publisher = {IEEE},
              year = {2012},
              pages = {3050 - 3055},
              url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=6315252&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D6315252},
              doi = {10.1109/ACC.2012.6315252}
            }
          
Abstract: In large energy systems, peak demand might cause severe issues such as service disruption and high cost of energy production and distribution. Under the widely adopted peak-demand pricing policy, electricity customers are charged a very high price for their maximum demand to discourage their energy usage in peak load conditions. In buildings, peak demand is often the result of temporally correlated energy demand surges caused by uncoordinated operation of subsystems such as heating, ventilating, air conditioning and refrigeration (HVAC&R) systems and lighting systems. We have previously presented green scheduling as an approach to schedule the building control systems within a constrained peak demand envelope while ensuring that custom climate conditions are facilitated. This paper provides a sufficient schedulability condition for the peak constraint to be realizable for a large and practical class of system dynamics that can capture certain nonlinear dynamics, inter-dependencies, and constrained disturbances. We also present a method for synthesizing periodic schedules for the system. The proposed method is demonstrated in a simulation example to be scalable and effective for a large-scale system.
[3] Truong X. Nghiem, Madhur Behl, Rahul Mangharam and George J. Pappas (2011), "Green scheduling of control systems for peak demand reduction", In 50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC),. , pp. 5131 - 5136. IEEE. Orlando, USA
BibTeX:
              @conference{NghiemEtAl11gso,
              author = {Truong X. Nghiem and Madhur Behl and Rahul Mangharam and George J. Pappas},
              title = {Green scheduling of control systems for peak demand reduction},
              booktitle = {50th IEEE Conference on Decision and Control and European Control Conference (CDC-ECC),},
              publisher = {IEEE},
              year = {2011},
              pages = {5131 - 5136},
              url = {http://repository.upenn.edu/cgi/viewcontent.cgi?article=1065&context=mlab_papers},
              doi = {10.1109/CDC.2011.6161164}
            }
          
Abstract: Building systems such as heating, air quality control and refrigeration operate independently of each other and frequently result in temporally correlated energy demand surges. As peak power prices are 200-400 times that of the nominal rate, this uncoordinated activity is both expensive and operationally inefficient. We present an approach to fine-grained coordination of energy demand by scheduling the control systems within a constrained peak while ensuring custom climate environments are facilitated. The peak constraint is minimized for energy efficiency, while we provide feasibility conditions for the constraint to be realizable by a scheduling policy for the control systems. The physical systems are then coordinated by the scheduling controller so as both the peak constraint and the climate/safety constraint are satisfied. We also introduce a simple scheduling approach called lazy scheduling. The proposed control and scheduling strategy is implemented in simulation examples from small to large scales, which show that it can achieve significant peak demand reduction while being efficient and scalable.
[2] Zheng Li, Pei-Chi Huang, Aloysius Mok, Truong Nghiem, Madhur Behl, George Pappas and Rahu Mangharam (2011), "On the feasibility of linear discrete-time systems of the green scheduling problem", In IEEE 32nd Real-Time Systems Symposium (RTSS),. , pp. 295 -304. Vienna, Austria.
BibTeX:
              @inproceedings{6121447,
              author = { Zheng Li and Pei-Chi Huang and Aloysius Mok and Truong Nghiem and Madhur Behl and George Pappas and Rahul Mangharam},
              title = {On the feasibility of linear discrete-time systems of the green scheduling problem},
              booktitle = {IEEE 32nd Real-Time Systems Symposium (RTSS),},
              publisher = {IEEE},
              year = {2011},
              pages = {295 -304},
              url = {http://georgejpappas.org/papers/06121447.pdf},
              doi = {10.1109/RTSS.2011.34}
            }
          
Abstract: Peak power consumption of buildings in large facilities like hospitals and universities becomes a big issue because peak prices are much higher than normal rates. During a power demand surge an automated power controller of a building may need to schedule ON and OFF different environment actuators such as heaters and air quality control while maintaining the state variables such as temperature or air quality of any room within comfortable ranges. The green scheduling problem asks whether a scheduling policy is possible for a system and what is the necessary and sufficient condition for systems to be feasible. In this paper we study the feasibility of the green scheduling problem for HVAC(Heating, Ventilating, and Air Conditioning) systems which are approximated by a discrete-time model with constant increasing and decreasing rates of the state variables. We first investigate the systems consisting of two tasks and find the analytical form of the necessary and sufficient conditions for such systems to be feasible under certain assumptions. Then we present our algorithmic solution for general systems of more than 2 tasks. Given the increasing and decreasing rates of the tasks, our algorithm returns a subset of the state space such that the system is feasible if and only if the initial state is in this subset. With the knowledge of that subset, a scheduling policy can be computed on the fly as the system runs, with the flexibility to add power-saving, priority-based or fair sub-policies.
[1] Truong X. Nghiem, Madhur Behl, George J. Pappas and Rahul Mangharam (2011), "Green scheduling: Scheduling of control systems for peak power reduction", In International Green Computing Conference and Workshops (IGCC),. , pp. 1 - 8. IEEE. Orlando, USA.
BibTeX:
              @conference{NghiemEtAl11gss,
              author = {Truong X. Nghiem and Madhur Behl and George J. Pappas and Rahul Mangharam},
              title = {Green scheduling: Scheduling of control systems for peak power reduction},
              booktitle = {International Green Computing Conference and Workshops (IGCC),},
              publisher = {IEEE},
              year = {2011},
              pages = {1 - 8},
              url = {http://repository.upenn.edu/cgi/viewcontent.cgi?article=1036&context=mlab_papers},
              doi = {10.1109/IGCC.2011.6008555}
            }
          
Abstract: Heating, cooling and air quality control systems within buildings and datacenters operate independently of each other and frequently result in temporally correlated energy demand surges. As peak power prices are 200-400 times that of the nominal rate, this uncoordinated activity is both expensive and operationally inefficient. While several approaches for load shifting and model predictive control have been proposed, we present an alternative approach to fine-grained coordination of energy demand by scheduling energy consuming control systems within a constrained peak power while ensuring custom climate environments are facilitated. Unlike traditional real-time scheduling theory, where the execution time and hence the schedule are a function of the system variables only, control system execution (i.e. when energy is supplied to the system) are a function of the environmental variables and the plant dynamics. To this effect, we propose a geometric interpretation of the system dynamics, where a scheduling policy is represented as a hybrid automaton and the scheduling problem is presented as designing a hybrid automaton. Tasks are constructed by extracting the temporal parameters of the system dynamics. We provide feasibility conditions and a lazy scheduling approach to reduce the peak power for a set of control systems. The proposed model is intuitive, scalable and effective for the large class of systems whose state-time profile can be linearly approximated.

Workshop and Demo Papers

[10] Achin Jain, Madhur Behl, Rahul Mangharam (2016), "Data Predictive Control for Building Energy Management: Poster Abstract", Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments, Palo Alto, CA, USA.
BibTeX:
              @inproceedings{Jain:2016:DPC:2993422.2996410,
 				author = {Jain, Achin and Behl, Madhur and Mangharam, Rahul},
 				title = {Data Predictive Control for Building Energy Management: Poster Abstract},
 				booktitle = {Proceedings of the 3rd ACM International Conference on Systems for Energy-Efficient Built Environments},
 				series = {BuildSys '16},
 				year = {2016},
 				isbn = {978-1-4503-4264-3},
 				location = {Palo Alto, CA, USA},
 				pages = {245--246},
 				numpages = {2},
 				url = {http://doi.acm.org/10.1145/2993422.2996410},
 				doi = {10.1145/2993422.2996410},
 				acmid = {2996410},
 				publisher = {ACM},
 				address = {New York, NY, USA},
				} 
          
Abstract: Decisions on how best to optimize today's energy systems operations are becoming ever so complex and conflicting such that model-based predictive control algorithms must play a key role. However, learning dynamical models of energy consuming systems such as buildings, using grey/white box approaches is very cost and time prohibitive due to its complexity. This paper presents data-driven methods for making control-oriented model for peak power reduction in buildings. Specifically, a data predictive control with regression trees (DPCRT) algorithm, is presented. DPCRT is a finite receding horizon method, using which the building operator can optimally trade off peak power reduction against thermal comfort without having to learn white/grey box models of the systems dynamics. We evaluate the performance of our method using a DoE commercial reference virtual test-bed and show how it can be used for learning predictive models with 90% accuracy, and for achieving 8.6% reduction in peak power and costs.
[9] Madhur Behl and Rahul Mangharam (2016), "Interactive Analytics for Smart Cities Infrastructures", In First International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE). [Under Review]
BibTeX:
              @conference{scope16,
              author = {Madhur Behl and Rahul Mangharam},
              title = {Interactive Analytics for Smart Cities Infrastructures},
              booktitle = {First International Workshop on Science of Smart City Operations and Platforms Engineering (SCOPE)},
              year = {2016}
            }
          
Abstract: Infrastructures in smart cities are complex systems, more than the sum of their parts, and operate through a multitude of individual and collective decisions. These systems are experiencing a gradual but substantial change in moving away from being non-interactive and manually-controlled sys- tems to utilizing tight integration of both cyber (computation, communications, and control) and physical representations guided by first principles, at all scales and levels. As a result, facility managers for such systems are demanding more control over the system operation. They prefer data-driven insights into performance and usage across their portfolios to make more effective decisions. Such insights help determine, if the system is operating efficiently and enable the facilities manager/system operator to investigate areas for improvement and evaluate upgrades. In this paper we describe one such system, which uses data-driven control-oriented models to build a data intelligence layer for the underlying physical infrastructure. Our system provides interactive analytics for the operator by answering queries and making recommendations about the systems oper- ation. We extend our previous work with using regression trees ensembles for predictive modeling of these large and ’messy’ systems, and show how tree based models can be converted into a knowledge discovery database. We present preliminary results for this system using data from a large office building.
[8] Madhur Behl Meghan Clark Alexandre Donzé Prabal Dutta Patrick Lazik Mehdi Maasoumy Rahul Mangharam Truong X Nghiem Vasumathi Raman Anthony Rowe Alberto Sangiovanni-Vincentelli Sanjit Seshia Tajana Simunic Rosing Jagannathan Venkatesh Baris Aksanli Alper S Akyurek (2014), "Demo Abstract: Distributed Control of a Swarm of Buildings Connected to Smart Grid", In Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings. , pp. 172-173. Memphis, USA.
BibTeX:
              @proceedings{BarisAlperBehlEtAl2014,
              author = {Baris Aksanli, Alper S Akyurek, Madhur Behl, Meghan Clark, Alexandre Donzé, Prabal Dutta, Patrick Lazik, Mehdi Maasoumy, Rahul Mangharam, Truong X Nghiem, Vasumathi Raman, Anthony Rowe, Alberto Sangiovanni-Vincentelli, Sanjit Seshia, Tajana Simunic Rosing, Jagannathan Venkatesh},
              title = {Demo Abstract: Distributed Control of a Swarm of Buildings Connected to Smart Grid},
              booktitle = {Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings},
              year = {2014},
              pages = {172-173},
              url = {http://escholarship.org/uc/item/8sd303p9.pdf}
            }
          
Abstract: The demo illustrates first a method for measuring the effects of individual building management tools on the grid and provide this information to the tools and where warranted, to the building operators to help finding corrective actions. By using a simulation tool, we can forecast any possible instability event before it happens and warn each tool if their actions are likely to affect negatively the grid.
[7] Madhur Behl, Neel D. Shah, Larry Vadakedathu, Dan Wheeler and Rahul Mangharam (2013), "Demo Abstract: EnergyLab: Building Energy Testbed for Demand-response", In 2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN). , pp. 303-304. Philadelphia, USA.
BibTeX:
              @conference{BehlShahVadakedathuEtAl2013,
              author = {Madhur Behl and Neel D. Shah and Larry Vadakedathu and Dan Wheeler and Rahul Mangharam},
              title = {Demo Abstract: EnergyLab: Building Energy Testbed for Demand-response},
              booktitle = {2013 ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN)},
              year = {2013},
              pages = {303--304},
              url = {https://www.infona.pl/resource/bwmeta1.element.ieee-art-000006917555},
              doi = {10.1109/IPSN.2013.6917555}
            }
          
Abstract: A building testbed for design and evaluation of energy efficient control and demand response strategies for real buildings is presented. The testbed is a scaled down model of a centralized Heating, Ventilation and Air Conditioning (HVAC) and lighting system. Sensing and control in the tesbed is achieved using the standard Building Automation and Control Network protocol. A MATLAB based front-end can be used to run and observe experiments.
[6] Truong Nghiem Rahul Mangharam Willy Bernal Madhur Behl (2012), "Demo Abstract: MLE+: Design and Deployment Integration for Energy-Efficient Building Controls", In Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings. , pp. 215-216. Toronto, Canada. [Best Demo Award]
BibTeX:
              @proceedings{mledemo,
              author = {Willy Bernal, Madhur Behl, Truong Nghiem, Rahul Mangharam},
              title = {MLE+: Design and deployment integration for energy-efficient building controls},
              booktitle = {Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings},
              publisher = {ACM New York, NY, USA ©2012},
              year = {2012},
              pages = {215-216},
              url = {http://dl.acm.org/citation.cfm?id=2422577},
              doi = {10.1145/2422531.2422577}
            }
          
Abstract: While building simulation software tools are excellent at carrying out accurate and realistic building simulations they only provide very basic control methods. On the other hand, control engineers and researchers have explored ad- vanced control strategies for energy-efficient operation of a building, but more often than not, such methods are based on simplified physical models instead. We present MLE+, a tool for energy-efficient building automation design, co- simulation and analysis. The tool leverages the high-fidelity plant simulation capabilities of building modeling software, EnergyPlus, and the scientific computation and design capa- bilities of Matlab/Simulink for controller design. MLE+ fa- cilitates integrated building simulation and controller formu- lation with integrated support for system identification, con- trol design, optimization, simulation analysis and commu- nication between software applications and building equip- ment. In this demo, we will show how MLE+ can be used for systematic design of controllers for energy-efficient building model in EnergyPlus. We will also show the capability of MLE+ to control Heating Ventilation and Air Conditioning (HVAC) devices in buildings through the BACnet protocol.
[5] Willy Bernal, Madhur Behl, Truong Nghiem and Rahul Mangharam (2012), "MLE+: A Tool for Integrated Design and Deployment of Energy Efficient Building Controls", In ACM SIGBED Review - Special Issue on the Work-in-Progress (WiP) session of the 33rd IEEE Real-Time Systems Symposium (RTSS'12). Vol. Volume 10(2), pp. 34-34. San Juan, Puerto Rico.
BibTeX:
              @proceedings{BernalBehlNghiemEtAl2012,
              author = {Willy Bernal and Madhur Behl and Truong Nghiem and Rahul Mangharam},
              title = {MLE+: A Tool for Integrated Design and Deployment of Energy Efficient Building Controls},
              booktitle = {ACM SIGBED Review - Special Issue on the Work-in-Progress (WiP) session of the 33rd IEEE Real-Time Systems Symposium (RTSS'12)},
              year = {2012},
              volume = {Volume 10},
              number = {2},
              pages = {34-34},
              url = {https://dl.acm.org/purchase.cfm?id=2518172&CFID=565508485&CFTOKEN=79545605},
              doi = {10.1145/2518148.2518172}
            }
          
[4] Madhur Behl, Mansimar Aneja, Harsh Jain and Rahul Mangharam (2011), "EnRoute: An energy router for energy-efficient buildings", In 10th International Conference on Information Processing in Sensor Networks (IPSN), 2011. , pp. 125 - 126. IEEE. Chicago, USA.
BibTeX:
              @conference{BehlAnejaJainEtAl2011,
              author = {Madhur Behl and Mansimar Aneja and Harsh Jain and Rahul Mangharam},
              title = {EnRoute: An energy router for energy-efficient buildings},
              booktitle = {10th International Conference on Information Processing in Sensor Networks (IPSN), 2011},
              publisher = {IEEE},
              year = {2011},
              pages = {125 - 126},
              url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5779077&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5779077}
            }
          
Abstract: The US Department of Energy estimates that 73% of electricity usage is consumed by buildings. The Heating, Ventilation and Air Conditioning (HVAC) systems account for almost 50% of the total energy budget. Demand-based pricing for commercial buildings makes it necessary for them to reduce power consumption during peak hours, when electricity is priced at a higher rate. Peaks in electricity demand occur when multiple systems are simultaneously consuming electricity. In this paper, we look at the problem of energy load management for commercial buildings and propose En-Route, a wireless Energy Router platform for control and scheduling of energy delivery to minimize peak electricity consumed by buildings. We have built a scaled model of a building, which generates HVAC dynamics that match an actual building. The Energy Router monitors the temperature, humidity and power consumption in different zones of the building in real-time. It then runs an “energy aware” algorithm that schedules HVAC systems based on the operating conditions of each zone to minimize the peak power consumption of the building.
[3] Utsav Drolia, Z. Wang, Srinivas Vemuri, Madhur Behl and Rahul Mangharam (2011), "Demo abstract: AutoPlug — An automotive test-bed for ECU testing, validation and verification", In 10th International Conference on Information Processing in Sensor Networks (IPSN),. , pp. 131 - 132. IEEE. Chicago, USA.
BibTeX:
              @conference{DroliaWangVemuriEtAl2011,
              author = {Utsav Drolia and Z. Wang and Srinivas Vemuri and Madhur Behl and Rahul Mangharam},
              title = {Demo abstract: AutoPlug — An automotive test-bed for ECU testing, validation and verification},
              booktitle = {10th International Conference on Information Processing in Sensor Networks (IPSN),},
              publisher = {IEEE},
              year = {2011},
              pages = {131 - 132},
              url = {http://ieeexplore.ieee.org/xpl/login.jsp?tp=&arnumber=5779080&url=http%3A%2F%2Fieeexplore.ieee.org%2Fxpls%2Fabs_all.jsp%3Farnumber%3D5779080}
            }
          
Abstract: In 2010, over 20.3 million vehicles were recalled. Software issues related to automotive controls such as to cruise control, anti-lock braking system, traction control and stability control, account for an increasingly large percentage of the overall vehicles recalled. We have developed AutoPlug, an automotive Electronic Controller Unit (ECU) test-bed to diagnose, test, validate and verify controls software. AutoPlug consists of multiple ECUs interconnected by a CAN bus, a race car driving simulator which behaves as the plant model and a vehicle safety and performance monitor in Matlab. As the ECUs drive the simulated vehicle, the physics-based simulation provides feedback to the controllers in terms of acceleration, yaw, friction and vehicle stability. This closed-loop platform is then used to evaluate multiple vehicle control software modules such as traction, stability and cruise control. With this test-bed we are aim to develop ECU software diagnosis and testing to evaluate the effect on the stability and performance of the vehicle. Code updates can be executed via a smart phone so drivers may remotely “patch” their vehicle. We will demonstrate a functioning closed-loop automotive test-bed to show the capabilities and challenges of remote code updates for vehicle recalls management.
[2] Madhur Behl and Rahul Mangharam (2010), "Pacer Cars: Real-Time Traffic Shockwave Suppression", In In Proceedings of the 32nd IEEE Real-Time Systems Symposium (Work in Progress session - RTSS11-WiP). IEEE. San Diego, USA.
BibTeX:
              @inproceedings{BehlMangharam2010,
              author = {Madhur Behl and Rahul Mangharam},
              title = {Pacer Cars: Real-Time Traffic Shockwave Suppression},
              booktitle = {In Proceedings of the 32nd IEEE Real-Time Systems Symposium (Work in Progress session - RTSS11-WiP)},
              publisher = {IEEE},
              year = {2010},
              url = {http://www.seas.upenn.edu/~mbehl/pubs/pacer.pdf}
            }
          
Abstract: ‘Stop-and-go’ waves or traffic shockwaves is a well analyzed traffic phenomenon and a known cause of traffic congestion. We propose Pacer Cars, a practical, low-cost and infrastructure-less technique to increase highway traffic capacity by alleviating and preventing traffic shockwaves. Pacer Cars are special cars, such that other cars are prohibited from overtaking a Pacer Car. We show that by injecting Pacer Cars into traffic streams on freeways, we can reduce the inflow of vehicles into a traffic shockwave, and prevent traffic congestion from propagating. We formulate traffic shockwave suppression as a control and scheduling problem and present a generic framework that is both practical and effective in suppressing shock waves. We demonstrate how reducing traffic shockwaves can increase traffic throughput without additional infrastructure investment.
[1] Madhur Behl, Willy Bernal ,Truong Nghiem, Miroslav Pajic and Rahul Mangharam (2010), "From Control to Scheduling: An Elastic Execution Model", In In Proceedings of the 32nd IEEE Real-Time Systems Symposium (Work in Progress session - RTSS11-WiP).
BibTeX:
              @inproceedings{BehlBernalMangharam2010,
              author = {Madhur Behl, Willy Bernal, Truong Nghiem, Miroslav Pajic and Rahul Mangharam},
              title = {From Control to Scheduling: An Elastic Execution Model},
              booktitle = {In Proceedings of the 32nd IEEE Real-Time Systems Symposium (Work in Progress session - RTSS11-WiP)},
              year = {2010},
              url = {http://cse.unl.edu/~rtss2008/WIP2010/8.pdf}
            }
          
Abstract: We present an elastic execution model for scheduling control systems while maintaining an acceptable level of service. Scheduling allows for coordination, composition and optimization across multiple interacting and non-interacting control systems. Unlike traditional real-time scheduling theory, where the execu- tion time, and hence the schedule, are a function of the system variables only, elastic execution time models found in Cyber- Physical Systems are a function of the physical variables and the dynamics of the system that is being controlled. A task-set is constructed by extracting the temporal parameters of a system from its dynamics. We then provide the conditions for feasibility, optimality and admissibility for a set of tasks. One application will be to implement an ‘energy-router’, which coordinates the electrical demand from multiple control systems to minimize the peak-to-average ratio of energy consumption in buildings. Our work is a step towards developing scheduling theory for cyber- physical systems.

Patents

Madhur Behl and Rahul Mangharam (2015), "METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR A DATA- DRIVEN DEMAND RESPONSE (DR) RECOMMENDER" United States Provisional Patent Application Serial No. 62/267,817 . Filed December 15, 2015.
BibTeX:
              @patent{behl_patent,
              author = {Madhur Behl and Rahul Mangharam},
              title = {METHODS, SYSTEMS, AND COMPUTER READABLE MEDIA FOR A DATA- DRIVEN DEMAND RESPONSE (DR) RECOMMENDER},
              year = {2015},
              number = {62267817}
            }
          

Technical Reports

[3] Madhur Behl and Rahul Mangharam (2015), "Evaluation of DR-Advisor on the ASHRAE Great Energy Predictor Shootout Challenge". Technical report at: University of Pennsylvania.
BibTeX:
              @techreport{behl_ashrae,
              author = {Madhur Behl and Rahul Mangharam},
              title = {Evaluation of DR-Advisor on the ASHRAE Great Energy Predictor Shootout Challenge},
              school = {University of Pennsylvania},
              year = {2015},
              url = {http://repository.upenn.edu/cgi/viewcontent.cgi?article=1093&context=mlab_papers}
            }
          
Abstract: This report describes the evaluation of DR-Advisor algorithms on '' The Great Energy Predictor Shootout - The First Building Data Analysis and Prediction Competition'' held in 1993-94 by ASHRAE.
[2] Madhur Behl, Truong Nghiem, and Rahul Mangharam (2013), "Uncertainty Propagation from Sensing to Modeling and Control in Buildings-Technical Report.". Technical report at: University of Pennsylvania.
Abstract: One of the biggest challenges in the domain of cyber physical energy systems is in accurately capturing the dy- namics of the underlying physical system. In the context of buildings, the modeling difficulty arises due to the fact that each building is designed and used in a different way and therefore, it has to be uniquely modeled. Furthermore, each building system is a collection of a large number of interconnected subsystems which interact in a complex manner and are subjected to time varying environmental conditions.
BibTeX:
              @techreport{uncer_behl,
              author = {Madhur Behl and Truong Nghiem, and Rahul Mangharam},
              title = {Uncertainty Propagation from Sensing to Modeling and Control in Buildings-Technical Report.},
              school = {University of Pennsylvania},
              year = {2013}
            }
          
[1] Madhur Behl (2008), "Mobility Modeling of Swarm Robots". Semester Thesis at: Punjab Engineering College and ETH Zurich.
BibTeX:
              @techreport{behl2008mobility,
              author = {Madhur Behl},
              title = {Mobility Modeling of Swarm Robots},
              school = {Punjab Engineering College and ETH Zurich},
              year = {2008},
              url = {http://www.seas.upenn.edu/~mbehl/pubs/eth.pdf}
            }
          
Abstract: Autonomous robotics is an area that attracts significant research interest in the robotics knowledge domain. The use of swarm robots for communication and networking purposes is one of their possible applications. The standard algorithms controlling the trajectories of these robot swarms aim at maintaining a given swarm formation in the 2D or even 3D space. Collision avoidance and repulsion are the main operations coming under the control of those algorithms. When looking at these robots from a purely networking point of view one question that becomes relevant is how could this structured movement of the swarm robots be best simulated and to what extent could it be approximated by existing or to- be-developed random group mobility models. The project builds on work on: - Group mobility modeling in 2D and 3D space: constraints are different than the movement within buildings, which has been the main focus of the rather limited 3D mobility modeling work so far - Robot swarm movement control: here basic control algorithms addressing collision avoidance and repulsion in 2D space may have to be extended to also address movement in 3D space.