Search

Abstract: The performance of different HVAC systems varies when coupled with different buildings. This paper examines the relationship between building heating and cooling load and subsequent energy consumption with different HVAC systems. Two common HVAC systems in use throughout the UK office building stock, variable air volume (VAV) system and fan coil (FC) with dedicated outside air system, have been coupled with a typical narrow plan office building with and without daylight control and for both cellular and open plan. The results presented in this paper clearly indicate that it is not possible to form a reliable judgment about building energy performance based only on building heating and cooling loads. For the two investigated systems, variable air volume system and fan coil with dedicated outside air system, the difference between system demand and building demand varied from over −40% to almost +30% for cooling and between −20% and +15% for heating. If a heat recovery unit is used, the difference in heating performance is even greater, rising to −70%. [Copyright &y& Elsevier]

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]

Abstract: The large thermal time constant of thermally activated building systems (TABS) hampers communication between the system''s production and emission. Therefore, conventional building control strategies, typically using room temperature feedback, are unadapted to control thermal comfort efficiently. In this paper, measurement data and simulation results reveal that unadapted TABS control has a dramatic impact on overall energy performance. Measurements in a TABS building with room temperature feedback show the HVAC system switching between heating and cooling in a very short time frame. A simplified, generic room model is used to simulate, understand and evaluate this behaviour. For room temperature feedback control, only 45% of the cold and 15% of the heat produced actually controls room temperature. The remainder is stored in the TABS and exchanged between the heating and cooling system. Enlarging the heating-to-cooling set point band improves this ratio, while maintaining thermal comfort. On the other hand, night time operation control of the circulation pump, adapted to the TABS thermal time constant, eliminates this ‘unused’ energy completely. In this case, however, even with perfect heat gain forecasts, it is difficult to avoid room temperatures dropping below thermal comfort limits during initial office hours. Hence, a supplementary air-conditioning system seems inevitable. [Copyright &y& Elsevier]

• Control strategy of the heating of an all-air system in an educational building is optimized. • A nearly zero energy educational building in Belgium is used as case study. • Winter monitoring campaign is analysed and a building energy simulation is calibrated. • Climatic control of the boiler causes a decrease of comfort hours up to 23%. • Climatic control of the storage tank determines energy saving until up to 47%. The optimal control of the HVAC system in the nearly zero energy buildings (nZEBs) can be considered as a challenge. Indeed, the lack of data given by monitored data does not allow understanding what are the implications of different strategies on thermal comfort and energy consumptions. This paper determines an approach for the evaluation of the management of the heating system in an existing educational nZEB. The aim is to understand how the energy efficiency of the heating system is sensitive to controls strategies and the important role played by the control systems. The test lecture rooms on KU Leuven Ghent Technology Campus (Belgium) have been chosen as case study. A deep analysis, starting from an in-field monitoring, was carried out during the winter period of 2019, in order to assess the actual operation and the energy use of building systems under real conditions. Then, a building energy simulation (BES) model of the building and its systems was developed and calibrated against the real monitoring data. Control strategies of the heating system are simulated and compared to the current configuration. The simulation results show that strategies based on a definition of a heating curve for the boiler can reduce the comfortable time (−23%). Controlling the temperature inside the storage tank as a function of the outdoor temperature together with the control on the activation of the air handling unit result in an energy savings from 32% to 46%, with a correspondent increase of comfort time in the period from 0.6% to 3.4% compared to the baseline scenario, depending to the relationship between the external temperature and water temperature inside the water storage tank. [ABSTRACT FROM AUTHOR]