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Disciplines
Controls and Control Theory
Electrical and Electronics
Power and Energy
Robotics
Systems Engineering
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Position
PhD Candidate
Introduction
I am a Ph.D. candidate in the Department of Electrical and Systems Engineering in the School of Engineering and Applied Science at University of Pennsylvania. I am a member of the Real Time and Embedded Systems Lab at UPenn. I work in the area of real time and embedded systems, energy-efficient operation of buildings ,integration of control and scheduling theory and cyber physical systems. I have added interest in networked robotics, vehicular networks, automotive embedded systems, machine learning and embedded systems for medical devices.
Research Interests

Search Results

Now showing 1 - 10 of 17
  • Publication
    Data-Driven Modeling, Control and Tools for Cyber-Physical Energy Systems
    (2016-04-01) Behl, Madhur; Jain, Achin; Mangharam, Rahul
    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 in- volve 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. 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 out- performs 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% pre- diction accuracy for 8 buildings on Penn’s campus. We com- pare DR-Advisor with other data driven methods and rank 2nd on ASHRAE’s benchmarking data-set for energy predic- tion.
  • Publication
    Evaluation of DR-Advisor on the ASHRAE Great Energy Predictor Shootout Challenge
    (2014-07-24) Behl, Madhur; Mangharam, Rahul
    This paper 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.
  • Publication
    DR-Advisor: A Data-Driven Demand Response Recommender System
    (2016-01-01) Behl, Madhur; Smarra, Francesco; Mangharam, Rahul
    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 main- taining 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.
  • Publication
    Green Scheduling for Radiant Systems in Buildings
    (2012-10-01) Nghiem, Truong X; Behl, Madhur; Pappas, George J.; Mangharam, Rahul
    In this report 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 report 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.
  • Publication
    Sometimes, Money Does Grow on Trees: DR-Advisor, A Data Driven Demand Response Recommender System
    (2015-09-01) Behl, Madhur; Mangharam, Rahul
    Unprecedented amounts of information from millions 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.
  • Publication
    Green Scheduling for Energy-Efficient Operation of Multiple Chiller Plants
    (2012-10-01) Behl, Madhur; Nghiem, Truong; Mangharam, Rahul
    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 electricity 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 technical report, 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.
  • Publication
    DR-Advisor: A Data Driven Demand Response Recommender System
    (2015-09-01) Behl, Madhur; Nghiem, Truong X; Mangharam, Rahul
    A data-driven method for demand response baselining and strategy evaluation is presented. 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 control 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. Keywords: demand response, regression trees, machine learning
  • Publication
    Green Scheduling: Scheduling of Control Systems for Peak Power Reduction
    (2011-07-01) Nghiem, Truong; Behl, Madhur; Pappas, George; Mangharam, Rahul
    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.
  • Publication
    Model-IQ: Uncertainty Propagation from Sensing to Modeling and Control in Buildings.
    (2014-04-01) Behl, Madhur; Nghiem, Truong; Mangharam, Rahul
    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.
  • Publication
    IMpACT: Inverse Model Accuracy and Control Performance Toolbox for Buildings
    (2014-03-01) Behl, Madhur; Nghiem, Truong X; Mangharam, Rahul
    Uncertainty affects all aspects of building performance: from the identification 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 effect 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 fixed 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.