Analysis of energy savings potentials for integrated room automation

The energy savings potential of selected low-cost measures related to the simultaneous control of blinds, electric lighting, heating, cooling and ventilation in a single building zone (Integrated Room Automation) was investigated. The analysis was based on a factorial study comprising several thousands, whole-year hourly time step simulations. The largest energy savings potential was found for the use of CO2-controlled ventilation as opposed to non-air quality controlled ventilation (average savings of 13%–28%, depending on the building zone characteristics and the choice of technical building system), followed by a widening of the thermal comfort range by ~1.5 oC (6%–16%), and the allowance for night/weekend room temperature set-back (0%–18%). Substantial energy savings potentials were also detected for advanced control: readily realizable energy savings thanks to improved non-predictive control amounted to 1%–15%, and theoretical savings potentials for predictive control to 16%–41%. The found, large case-to-case variability surrounding these average numbers un- derlines the importance of simulation-based assessments on a per case basis

Increasing energy efficiency in building climate control using weather forecasts and model predictive control

This paper presents an investigation of how Model Predictive Control (MPC) and weather predictions can increase the energy efficiency in Integrated Room Automation (IRA) while respecting occupant comfort. IRA deals with the simultaneous control of heating, ventilation and air conditioning as well as blind positioning and electric lighting such that the room temperature as well as CO2 and luminance levels stay within given comfort ranges. MPC is an advanced control technique which, when applied to buildings, employs a model of the building dynamics and solves an optimization problem to determine the optimal control inputs. The result is an optimal plan in the sense that it takes into account the future weather and internal gains and controls the HVAC, light and blind units to minimize energy costs while respecting comfort constraints. Through a large-scale factorial simulation study we show that MPC coupled with weather predictions is beneficial in terms of energy efficiency and occupant comfort. In particular, we investigate the control performance, the impact of the accuracy of weather predictions as well as the robustness and tunability of the control strategy

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 building’s 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 building’s 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 building’s technical subsystems

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.ISSN:1063-6536ISSN:1558-086

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