Dimitrios Gyalistras

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

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.

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.

Modeling and identification of a large multi-zone office building

Predictive control in buildings has undergone an intensive research in the past years. Model identification plays a central role in a predictive control approach. This paper presents a comprehensive study of modeling of a large multi-zone office building. Many of the common methods used for modeling of the buildings, such as a detailed modeling of the physical properties, RC modeling, etc., appeared to be unfeasible because of the complexity of the problem. Moreover, most of the research papers dealing with this topic presents identification (and control) of either a single-zone building, or a single building sub-system. On contrary, we proposed a novel approach combining a detailed modeling by a building-design software with a black-box subspace identification. The uniqueness of the presented approach is not only in the size of the problem, but also in the way of getting the model and interconnecting several computational and simulation tools.

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.n 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.n 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.