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Abstract: This paper presents model predictive controller (MPC) applied to the temperature control of real building. Conventional control strategies of a building heating system such as weather-compensated control cannot make use of the energy supplied to a building (e.g. solar gain in case of sunny day). Moreover dropout of outside temperature can lead to underheating of a building. Presented predictive controller uses both weather forecast and thermal model of a building to inside temperature control. By this, it can utilize thermal capacity of a building and minimize energy consumption. It can also maintain inside temperature at desired level independent of outside weather conditions. Nevertheless, proper identification of the building model is crucial. The models of multiple input multiple output systems (MIMO) can be identified by means of subspace methods. Oftentimes, the measured data used for identification are not satisfactory and need special treatment. During the 2009/2010 heating season, the controller was tested on a large university building and achieved savings of 17–24% compared to the present controller. [ABSTRACT FROM AUTHOR]

Abstract: Low energy buildings have attracted lots of attention in recent years. Most of the research is focused on the building construction or alternative energy sources. In contrary, this paper presents a general methodology of minimizing energy consumption using current energy sources and minimal retrofitting, but instead making use of advanced control techniques. We focus on the analysis of energy savings that can be achieved in a building heating system by applying model predictive control (MPC) and using weather predictions. The basic formulation of MPC is described with emphasis on the building control application and tested in a two months experiment performed on a real building in Prague, Czech Republic. [Copyright &y& Elsevier]