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Abstract: HVAC (Heating, Ventilation and Air Conditioning) systems used for heating or cooling buildings, consume a considerable amount of energy. To optimize the energy consumption, the behavior of occupants must be changed. This can be achieved by providing information and suggestions to occupants. A first step is developing of a less expensive and non-invasive measurement system and metering of the electricity and heat consumed. Based on collected experimental data, it can identify the parameters of a thermal model of the house. The model obtained will be used to simulate different aspects that can help to reduce the energy consumption. This paper presents a simple solution for thermal modeling of a house which includes experimental identification of the model''s parameters. Such data are used to simulate the thermal behavior of the house and to obtain solutions to reduce energy consumption. In simulation, the control of the thermal system is performed using a model based predictive control algorithm. [Copyright &y& Elsevier]

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]