Markus Gwerder

Bio: Markus Gwerder is an academic researcher from Siemens Building Technologies. The author has contributed to research in topics: Model predictive control & HVAC. The author has an hindex of 7, co-authored 13 publications receiving 1607 citations. Previous affiliations of Markus Gwerder include Siemens.

Energy efficient building climate control using Stochastic Model Predictive Control and weather predictions

Abstract: One of the most critical challenges facing society today is climate change and thus the need to realize massive energy savings. Since buildings account for about 40% of global final energy use, energy efficient building climate control can have an important contribution. In this paper we develop and analyze a Stochastic Model Predictive Control (SMPC) strategy for building climate control that takes into account weather predictions to increase energy efficiency while respecting constraints resulting from desired occupant comfort. We investigate a bilinear model under stochastic uncertainty with probabilistic, time varying constraints. We report on the assessment of this control strategy in a large-scale simulation study where the control performance with different building variants and under different weather conditions is studied. For selected cases the SMPC approach is analyzed in detail and shown to significantly outperform current control practice.

Predictive control for integrated room automation

Abstract: In order to operate buildings more energy and cost effective, predictive integrated room automation can be used instead of conventional – possibly integrated – room automation. Thereby the predictive integrated room automation controllers operate the buildings’ passive thermal storages based on predicted future disturbances (e.g. weather forecast) by making use of low cost energy sources. A specific predictive integrated room automation application is considered here: The room temperature can – technically – be controlled by heating, cooling with chiller, free cooling and blind positioning. To satisfy the thermal comfort demand, the room temperature is controlled within a defined comfort range. This is achieved by a model predictive control strategy which makes use of the passive thermal storage of the building: To reduce the energy costs the thermal capacity of the building can be loaded or unloaded with low cost energy (free cooling, solar gains influenced by blinds) as long as the room temperature remains in the comfort range. The predictive controller periodically calculates an optimal future profile of the manipulated variables while constraints on the manipulated variables and predicted disturbances are taken into account. The optimization problem is solved numerically by applying linear programming (LP) algorithms. A performance bound is determined by simulations. Furthermore conventional (nonpredictive) control strategies are compared and assessed using the performance bound as a benchmark. These analyses show that predictive control is promising to be a substantial improvement compared to non-predictive control regarding cost and energy efficiency.

Model Predictive Control of a Swiss Office Building

Abstract: The research project OptiControl (www.opticontrol.ethz.ch) deals with the development of novel, predictive control strategies for buildings. The strategies are tested on a fully occupied, well instrumented typical Swiss office building. This work presents our experience with the application of Model Predictive Control (MPC). The application of novel rule-based control (RBC) strategies on the same building is presented in a companion paper (Integrated Predictive Rule-Based Control of a Swiss Office Building). Here we describe, first, the implementation and key aspects of model predictive building control. Second, we report on our experience with running the MPC controller on the building for three months. Third, we compare the controller’s performance in terms of comfort compliance and energy use to the previously installed industry standard RBC strategy using whole-year simulations with the EnergyPlus software. The experimental data show that the MPC operated reliably and successfully satisfied comfort constraints during a period of three months in summer. The simulation study suggests a superior control performance with respect to the original control strategy.

Potential Assessment of Rule-Based Control for Integrated Room Automation

Abstract: SUMMARY In the Swiss research project OptiControl, the use of weather and occupancy forecast for optimal building control is investigated. The paper presents one result of the project: A potential assessment of both non-predictive and predictive rule-based control for integrated room automation. Different rule-based control algorithms – still the standard approach in today’s building automation – are examined and compared in a large-scale simulation study. To our knowledge, no such systematic potential assessment has been carried out so far. Control performance is measured by non-renewable primary energy (NRPE) usage while thermal, luminance and air quality comfort is maintained within desired ranges. The control algorithms show large performance variations, both between each other and depending on individual cases. Blind operation restrictions are found to heavily impact the control performance. Comparisons of the control performances with the so-called performance bound (theoretical minimum NRPE usage) suggest substantial potential for further NRPE savings by advanced control.

A review on optimized control systems for building energy and comfort management of smart sustainable buildings

Abstract: Buildings all around the world consume a significant amount of energy, which is more or less one-third of the total primary energy resources. This has raised concerns over energy supplies, rapid energy resource depletion, rising building service demands, improved comfort life styles along with the increased time spent in buildings; consequently, this has shown a rising energy demand in the near future. However, contemporary buildings’ energy efficiency has been fast tracked solution to cope/limit the rising energy demand of this sector. Building energy efficiency has turned out to be a multi-faceted problem, when provided with the limitation for the satisfaction of the indoor comfort index. However, the comfort level for occupants and their behavior have a significant effect on the energy consumption pattern. It is generally perceived that energy unaware activities can also add one-third to the building’s energy performance. Researchers and investigators have been working with this issue for over a decade; yet it remains a challenge. This review paper presents a comprehensive and significant research conducted on state-of-the-art intelligent control systems for energy and comfort management in smart energy buildings (SEB’s). It also aims at providing a building research community for better understanding and up-to-date knowledge for energy and comfort related trends and future directions. The main table summarizes 121 works closely related to the mentioned issue. Key areas focused on include comfort parameters, control systems, intelligent computational methods, simulation tools, occupants’ behavior and preferences, building types, supply source considerations and countries research interest in this sector. Trends for future developments and existing research in this area have been broadly studied and depicted in a graphical layout. In addition, prospective future advancements and gaps have also been discussed comprehensively.

Stochastic Model Predictive Control: An Overview and Perspectives for Future Research

Abstract: Model predictive control (MPC) has demonstrated exceptional success for the high-performance control of complex systems [1], [2]. The conceptual simplicity of MPC as well as its ability to effectively cope with the complex dynamics of systems with multiple inputs and outputs, input and state/output constraints, and conflicting control objectives have made it an attractive multivariable constrained control approach [1]. MPC (a.k.a. receding-horizon control) solves an open-loop constrained optimal control problem (OCP) repeatedly in a receding-horizon manner [3]. The OCP is solved over a finite sequence of control actions {u0,u1,f,uN- 1} at every sampling time instant that the current state of the system is measured. The first element of the sequence of optimal control actions is applied to the system, and the computations are then repeated at the next sampling time. Thus, MPC replaces a feedback control law p(m), which can have formidable offline computation, with the repeated solution of an open-loop OCP [2]. In fact, repeated solution of the OCP confers an "implicit" feedback action to MPC to cope with system uncertainties and disturbances. Alternatively, explicit MPC approaches circumvent the need to solve an OCP online by deriving relationships for the optimal control actions in terms of an "explicit" function of the state and reference vectors. However, explicit MPC is not typically intended to replace standard MPC but, rather, to extend its area of application [4]-[6].