David Sturzenegger

Model Predictive Climate Control of a Swiss Office Building: Implementation, Results, and Cost–Benefit Analysis

This paper reports the final results of the predictive building control project OptiControl-II that encompassed seven months of model predictive control (MPC) of a fully occupied Swiss office building. First, this paper provides a comprehensive literature review of experimental building MPC studies. Second, we describe the chosen control setup and modeling, the main experimental results, as well as simulation-based comparisons of MPC to industry-standard control using the EnergyPlus simulation software. Third, the costs and benefits of building MPC for cases similar to the investigated building are analyzed. In the experiments, MPC controlled the building reliably and achieved a good comfort level. The simulations suggested a significantly improved control performance in terms of energy and comfort compared with the previously installed industry-standard control strategy. However, for similar buildings and with the tools currently available, the required initial investment is likely too high to justify the deployment in everyday building projects on the basis of operating cost savings alone. Nevertheless, development investments in an MPC building automation framework and a tool for modeling building thermal dynamics together with the increasing importance of demand response and rising energy prices may push the technology into the net benefit range.

Towards a standardized building assessment for demand response

This paper addresses the setup of an aggregation of office buildings to provide services for the electricity grid. In order to determine the energy shifting potential of different buildings for a given time of the day, a standardized assessment procedure based on Model Predictive Control and predefined price signals is proposed. The aim is to provide the aggregator with an index describing the additional energy used for a desired change in power consumption for each building and for each hour of the day, enabling him to choose a good portfolio of buildings for providing the grid service cost-effectively. We first show in an experiment on a real office building in Switzerland the possibility of shifting the electricity consumption as well as a comparison of experiments and simulations for this building case. We then use this building case to test the proposed procedure in simulation and provide an hourly analysis of the power shifting potential for different seasons.

BRCM Matlab Toolbox: Model generation for model predictive building control

Model predictive control (MPC) is a promising alternative in building control with the potential to improve energy efficiency and comfort and to enable demand response capabilities. Creating an accurate building model that is simple enough to allow the resulting MPC problem to be tractable is a challenging but crucial task in the control development. In this paper we introduce the Building Resistance-Capacitance Modeling (BRCM) Matlab Toolbox that facilitates the physical modeling of buildings for MPC. The Toolbox provides a means for the fast generation of (bi-)linear resistance-capacitance type models from basic building geometry, construction and systems data. Moreover, it supports the generation of the corresponding potentially time-varying costs and constraints. The Toolbox is based on previously validated modeling principles. In a case study a BRCM model was automatically generated from an EnergyPlus input data file and its predictive capabilities were compared to the EnergyPlus model. The Toolbox itself, the details of the modeling and the documentation can be found at www.brcm.ethz.ch.

Semi-automated modular modeling of buildings for model predictive control

A promising alternative to standard control strategies for heating, ventilation, air conditioning and blinds positioning of buildings is Model Predictive Control (MPC). Key to MPC is having a sufficiently simple (preferably linear) model of the buildings thermal dynamics. In this paper we propose and test a general approach to derive MPC compatible models consisting of the following steps: First, we use standard geometry and construction data to derive in an automated way a physical first-principles based linear model of the buildings thermal dynamics. This describes the evolution of room, wall, floor and ceiling temperatures on a per zone level as a function of external heat fluxes (e.g., solar gains, heating/cooling system heat fluxes etc.). Second, we model the external heat fluxes as linear functions of control inputs and predictable disturbances. Third, we tune a limited number of physically meaningful parameters. Finally, we use model reduction to derive a low-order model that is suitable for MPC. The full-scale and low-order models were tuned with and compared to a validated EnergyPlus building simulation software model. The approach was successfully applied to the modeling of a representative Swiss office building. The proposed modular approach flexibly supports stepwise model refinements and integration of models for the buildings technical subsystems.

Adaptively constrained Stochastic Model Predictive Control for closed-loop constraint satisfaction

Stochastic Model Predictive Control (SMPC) for discrete-time linear systems subject to additive disturbances with chance constraints on the states and hard constraints on the inputs is considered. Current chance constrained MPC methods-based on analytic reformulations or on sampling approaches-tend to be conservative partly because they fail to exploit the predefined violation level in closed-loop. For many practical applications, this conservatism can lead to a loss in performance. We propose an adaptive SMPC scheme that starts with a standard conservative chance constrained formulation and then on-line adapts the formulation of constraints based on the experienced violation frequency. Using martingale theory we establish guarantees of convergence to the desired level of constraint violation in closed-loop for a special class of linear systems. Comments are given on how to extend this to a broader class of (non-)linear systems. The developed methodology is demonstrated with an illustrative example.

A tracking algorithm for PTZ cameras

Abstract This paper presents a tracking algorithm for PTZ (pan-tilt-zoom) cameras. The tracked objects are Mini 1:43 scale RC cars that have been described by a unicycle model. The algorithm is based on the combination of EKF (Extended Kalman Filter) and PF (Particle Filters). A scanning procedure is used to explore the environment. Once targets are detected, EKF is used to predict their future position. PTZ are then moved in order to guarantee a certain probability of targets detection at the next time instant. If a target is lost, particle filters is exploited. If target is found again EKF is restored. If this does not happen in a predefined number of steps, the scanning procedure restarts.

Frequency-Domain Identification of a Ventilated Room for Model Based Control

Abstract Efficient and accurate modeling techniques have become increasingly important in the context of model predictive control (MPC) for building automation. For modeling single-input single-output systems such as a ventilated room (with either constant air flow or constant supply temperature), system identification methods are promising and provide insight into the physical nature of these systems. In collaboration with the company SAUTER an office type test room was instrumented for experiments. Three models for the room were derived: i) an empirical transfer function estimate (ETFE) derived from a pseudo-random binary sequence input signal; ii) an ETFE derived from a relay feedback approach; iii) a physics based resistance-capacitance (RC) model. Using additional validation data, the different models and approaches were compared in terms of accuracy and efficiency. The effect of air mixing dynamics was demonstrated in an additional experiment to be one of the main differences between the experimentally identified and the RC model. An additional pole can be added to the RC model in order to compensate for the differences.