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Building Control

One of the most critical challenges of this century is the increasing global demand for energy and the resulting impact on the environment. Worldwide, the residential and commercial sectors use 2589 Mtoe (mega tonnes of oil equivalent) in energy, which accounts for almost 40% of final energy use in the world [1]. In European countries, 76% of this energy goes towards comfort control in buildings - heating, ventilation and air conditioning (HVAC).

This level of consumption makes measures aimed at HVAC energy reduction very attractive. These can be realized by improving a building's HVAC systems and construction, its operation, or preferably some combination of both. Unfortunately, the majority of the building stock is already in place and refurbishments of buildings are expensive. Quite differently, control systems can be upgraded and their operation optimized at comparatively low cost. Our efforts focus on the development, simulation and implementation of model predictive control (MPC) for buildings.

MPC is a promising alternative to standard strategies for building control. It uses a mathematical model of the building and predictions of disturbances (e.g., ambient temperature) over a given prediction horizon (e.g., two days) for defining an optimization problem that is solved such as to maintain thermal comfort for the occupants while minimizing some objective (e.g., energy use or monetary cost). This makes it possible to integrate all available actuators and their interactions as well as predictions of weather, internal gains and electricity prices into a coherent, mathematical control framework that can handle constraints on control inputs and room temperatures. MPC relies on having a model of the building dynamics.

[1] International Energy Agency: Energy efficiency requirements in building codes, energy efficiency policies for new buildings


Building Control Group
Automatic Control Laboratory
Physikstrasse 3
CH-8092 Zürich

Building ETH Zurich, HCI
Figure 1: View at the buildings in ETH Hönggerberg. © Hannes Röst, CC by-sa