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

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 con-straints 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