Mehdi Maasoumy, Barzin Moridian, Meysam Razmara, Mahdi Shahbakhti and Alberto Sangiovanni-Vincentelli, "Online Simultaneous State Estimation and Parameter Adaptation for Building Predictive Control", Dynamic System and Control Conference (DSCC 2013), Stanford, CA, USA. Submitted. [ html ]

Abstract: Model-based control of building energy offers an attractive way to minimize energy consumption in buildings. Model-based controllers require mathematical models that can accurately predict the behavior of the system. For buildings, specifically, these models are usually difficult to obtain due to highly time varying, and nonlinear nature of building dynamics. Also, modelbased controllers often need information of all states, while not all the states of a building model are measurable. In addition, it is challenging to accurately estimate building model parameters (e.g. convective heat transfer coefficient of varying outside air). In this paper, we propose an adaptive modeling framework for “on-line estimation” of states and unknown parameters. Extended Kalman filter (EKF) and unscented Kalman filter (UKF) techniques are used to design an adaptive building model which simultaneously tunes the parameters of the model and provides an estimate for all states of the model. The proposed adaptive framework is tested with the experimental data collected from a university building. Our results indicate that the new framework can accurately predict state and parameters of the building thermal model. The new modeling framework is expected to simplify design of a building predictive control by replacing nonlinear terms in a control model with linear adaptive parameters.

Mehdi Maasoumy, Qi Zhu, Cheng Li, Forrest Meggers and Alberto Sangiovanni-Vincentelli, "Co-design of Control Algorithm and Embedded Platform for HVAC Systems", The 4th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS 2013), Philadelphia, USA. (BEST PAPER AWARD)[ html ] [ pdf ] [ bib ]

Abstract: The design of heating, ventilation and air conditioning (HVAC) systems is crucial for reducing the energy consumption in buildings. As complex cyber-physical systems, HVAC systems involve three closely-related subsystems - the control algorithm, the physical environment and the embedded implementation platform. In the traditional top-down approach, the control algorithm and the embedded platform are mostly designed separately leading to sub-optimal systems. In this paper, we propose a co-design approach that analyzes the interaction between the control algorithm and the embedded platform through a set of interface variables in particular the sensing accuracy. We design six control algorithms that take into account the sensing error, and model the relation of control performance and cost versus sensing error. We also capture the relation of embedded platform cost versus sensing error by analysis of the collected data from a testbed. Based on these models, we explore the co-design of the control algorithm and the embedded platform to optimize a system with respect to energy cost and monetary cost while satisfying the constraints for user comfort level.

Mehdi Maasoumy, Alberto Sangiovanni-Vincentelli, "Total and Peak Energy Consumption Minimization of Building HVAC Systems Using Model Predictive Control", IEEE Design & Test of Computers, Special Issus on Green Buildings, July/Aug 2012.
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Abstract: A non-linear model representing the thermal dynamics of buildings is developed which incorporates the thermal heat exchange between neighboring nodes (i.e. walls and rooms) in the building and the input conditioned air from the HVAC system, the internal heat loads from occupants and external heat gains. The proposed model is calibrated against historical data and the parameters. Un-modeled dynamics are identified using non-linear regression technique. Two different controllers are proposed to reduce the total energy consumption of the system and the maximum airflow rate of an HVAC system. The implemented model predictive controller achieves a reduction of 67.2% in total airflow input, 33.3% in the maximum airflow rate and 73.2% in total energy consumption with respect to the controller used presently in the building HVAC system.

Yang Yang, Qi Zhu, Mehdi Maasoumy, and Alberto Sangiovanni-Vincentelli, "Development of Building Automation and Control Systems", IEEE Design & Test of Computers, Special Issue on Green Buildings, July/Aug 2012. [ html ] [ pdf ] [ bib ]

Abstract: A Building Automation and Control (BAC) system is the brain of a modern building. It controls various aspects of the building operations including heating, ventilation, air conditioning (HVAC), lighting, fire and security. The design of BAC systems is crucial to building performance and energy efficiency. In this paper, we propose an automated system-level design flow for BAC systems that addresses heterogeneous input specification and implementation platform while performing automatic design space exploration and code generation. An intermediate format (IF) is defined as the central hub of the design flow to facilitate the integration of heterogeneous inputs, the leverage of back-end tools and the design space exploration. Case studies of a temperature control system are presented to demonstrate the flow.

Mehdi Maasoumy, Alberto Sangiovanni-Vincentelli, "Optimal Control of Building HVAC Systems in the Presence of Imperfect Predictions, Dynamic System Control Conference, Fort Lauderdale, FL, Oct 2012 [ pdf ]

Abstract: This paper deals with the problem of robust model predictive control of an uncertain linearized model of a building envelope and HVAC system. Uncertainty of the model is due to the imperfect predictions of internal and external heat gains of the building. The Open-Loop prediction formulation of the Robust Model Predictive Control (OL-RMPC) is known to be unnecessarily over-conservative in practice. Therefore, we adopt a Closed-Loop prediction formulation of Robust Model Predictive Control (CL-RMPC) which exploits an uncertainty feedback parameterization of the control sequence and results in a tractable formulation of the problem. To improve on the efficiency of CLRMPC we propose a new uncertainty feedback parameterization of the control input, which leads to a number of decision variables linear in time horizon as opposed to quadratic as in previous approaches. To assess our approach we compare three different robust optimal control strategies: nominal MPC which does not have a priori information of the uncertainty, OL-RMPC and CL-RMPC. We show results from a quantitative analysis of performance of these controllers at different prediction error values of the disturbance. Simulations show that CL-RMPC provides a higher level of comfort with respect to OL-RMPC while consuming 36% less energy. Moreover, CL-RMPC maintains perfect comfort level for up to 75% error in the disturbance prediction. Finally, the newly proposed parameterization maintains the performance of CL-RMPC while reducing the simulation time by an average of 30%.

Mehdi Maasoumy, Alessandro Pinto, Alberto Sangiovanni-Vincentelli, "Model-based Hierarchical Optimal Control Design for HVAC Systems" Dynamic System Control Conference, Arlington, VA 2011.[ html ] [ pdf ] [ bib ]

Abstract: A hierarchical control architecture for balancing comfort and energy consumption in buildings is presented. The control design is based on a simplified, yet accurate model of the temperature within each room of the building. The model is validated against real measurements. The control architecture comprises a first level that regulates low level quantities such as air flow, and a second level that balances comfort (i.e. distance between the desired and actual temperature) and energy consumption (i.e. total energy consumed for the required level of comfort). We show the effectiveness of our approach by simulation using validated models.

Mehdi Maasoumy, Alberto Sangiovanni-Vincentelli, "Building Operating Platform Design for High Performance Zero-Energy Buildings, Master's Thesis, University of California, Berkeley, May 2010 [ pdf ]

Abstract: We have Modeled the thermal behavior of a building using Nodal Circuit Analysis. A hierarchical optimal control algorithm was proposed which was shown to save energy while providing the desired comfort level by optimizing a cost function. We have Solved the optimization problem, using Dynamic Programming method to calculate the optimal input to the control system being the optimal mass air flow (MAF) through each duct. We have simulated the behavior of an 8-room building model using Simscape from Simulink. We have validated the Nodal Circuit Analysis approach using the temperature data of UC Berkeley, DOE library. At the end the results of the controller design are presented, which show how much energy is saved by using this control algorithm.

Mehdi Maasoumy "Construction of a flow model for assessing human motion in buildings", Class project [ pdf ]

Abstract: Sensor Utility Network (SUN) method for occupancy estimation in buildings through the solution of a receding horizon convex optimization problem was investigated. Belief Network method application of a class of graphical probability models called belief network for the purpose of prediction, diagnosis and etc was studied.

Mehdi Maasoumy "Potentials for energy savings in buildings by improving fans in HVAC systems", Class Project [ pdf ]

Abstract: It was shown that a 20% increase in fan efficiency will lead to almost 6% decrease in the energy that goes into HVAC. This is quite substantial, because the HVAC system accounts for one third of the total energy in a commercial building.

Mehdi Maasoumy "Nonlinear Controller Design for the HVAC system of Energy Efficient Buildings", Class Project [ pdf ]

Abstract: We have implemented input-output and input-state linearization techniques to transform the original system model into equivalent models of simpler forms. Theorem of Frobenius is used to determine the feasibility of input-state linearization. Sliding mode control was designed as a robust controller for the system.

Mehdi Maasoumy, "Optimal Control for the Operation of Building Cooling Systems with VAV Boxes", Class Project [ pdf ]

Abstract:s We have addressed two issues related to the cooling and heating system in Sutardja Dai Hall. First, the performance of the chilling system and heating system in Sutardja Dai Hall are analyzed in terms of Coefficient of Performance (COP). Second, we study the problem of temperature regulation in a network of building thermal zones. The control sequences for Air Handling Units (AHU) and Variable Air Volume (VAV) boxes are optimized by incorporating the predictive information of weather and occupancy and adopting Model Predictive Control (MPC).

Aircraft Load Management Systems:

Publications:

Mehdi Maasoumy, Pierluigi Nuzzo,Maryam Kamgarpour, Claire Tomlin, Alberto Sangiovanni-Vincentelli and Mohammad Mozumdar "OLMS: Optimal Load Management System for Aircraft Electric Power Distribution" In Progress..

Abstract: Aircraft Electric Power Systems (EPS) route power from generators to vital avionics loads by configuring a set of electronic control switches denoted as contactors. While EPS systems are increasingly more complex, their realization is still considered to be a derivative design process, where newly developed designs are often a reincarnation of older ones, due to the limited support for innovative designs offered by structured methodologies that could substantially reduce development risks. In this paper, we address the problem of designing an optimal control strategy for the EPS contactors. We first formalize the system connectivity, safety and performance requirements in terms of mathematical constraints. We then show that the EPS control problem can be formulated as a Mixed-Integer Linear Program and efficiently solved to yield load shedding, source allocation, contactor switching and battery charging policies, while optimizing a number of performance metrics, such as the number of used generators and shed loads. Our solution can be easily integrated into a more general hierarchical control scheme that accounts for system faults. Finally, we report simulation results to confirm the effectiveness of the proposed approach..

Smart Grid:

Publications:

Mehdi Maasoumy, Jorge Ortiz "Ancillary Services for the Grid Using Building Potentials: A Model Predictive Control Approach" In Progress...

Abstract: Traditionally, grid has been controlled using simple PI controllers. However, increasing penetration of intermittent and unpredictable renewable energy sources, poses challenges to the utility companies and to the stability of the grid. Buildings are accounted for 40% of total energy consumption, and the heat storage in buildings is an enormous untapped resource to provide regulation services to the grid. Regulation services from buildings (also known as ancillary services) can be obtained by exploiting the inherent flexibility of HVAC systems in commercial buildings, due to their large thermal inertia. We propose a model predictive control scheme to control the available ancillary service from buildings to improve on the classical PI control.

Chaos in Dynamical Systems:

Publications:

Zohreh Mohammadi, Azadeh Marouf Mashat, Hassan Salarieh, Mehdi Maasoumy, Mohammad Abediny, Aria Alasty "Experimental Investigating of non-linear and chaotic behavior of a doubly-clamped beam under electromagnetic excitation" [ pdf ]

Abstract: We have addressed two issues related to the cooling and heating system in Sutardja Dai Hall. First, the performance of the chilling system and heating system in Sutardja Dai Hall are analyzed in terms of Coefficient of Performance (COP). Second, we study the problem of temperature regulation in a network of building thermal zones. The control sequences for Air Handling Units (AHU) and Variable Air Volume (VAV) boxes are optimized by incorporating the predictive information of weather and occupancy and adopting Model Predictive Control (MPC).