Heating, Ventilation and Air Conditioning (HVAC) systems play a fundamental role in maintaining acceptable thermal comfort and Indoor Air Quality (IAQ) levels, essentials for occupants well-being. Since performing this task implies high energy requirements, there is a need for improving the energetic efficiency of existing buildings. A possible solution is to develop effective control strategies for HVAC systems, but this is complicated by the inherent uncertainty of the to-be-controlled system. To cope with this problem, we design a stochastic Model Predictive Control (MPC) strategy that dynamically learns the statistics of the building occupancy and weather conditions and uses them to build probabilistic constraints on the indoor temperature and CO 2 concentration levels. More specifically, we propose a randomization technique that finds suboptimal solutions to the generally non-convex stochastic MPC problem. The main advantage of this method is the absence of apriori assumptions on the distributions of the uncertain variables, and that it can be applied to any type of building. We investigate the proposed approach by means of numerical simulations and real * tests on a student laboratory, and show its practical effectiveness and computational tractability.
There is currently an increasing trend in Europe to build passive houses. In order to reduce the cost of installation, an air-heating system may be an interesting alternative. Heat supplied through ventilation ducts located at the ceiling was studied with computational fluid dynamics technique. The purpose was to illustrate the thermal indoor climate of the building. To validate the performed simulations, measurements were carried out in several rooms of the building. Furthermore, this study investigated if a designed passive house located above the Arctic Circle could fulfil heat requirements for a Swedish passive house standard. Our results show a heat loss factor of 18.8 W/m2 floor area and an annual specific energy use of 67.9 kWh/m2 floor area, would fulfils the criteria. Validation of simulations through measurements shows good agreement with simulations if the thermal inertia of the building was considered. Calculation of heat losses from a building with a backward weighted moving average outdoor temperature produced correct prediction of the heat losses. To describe the indoor thermal climate correctly, the entire volume needs to be considered, not only one point, which normally is obtained with building simulation software. The supply airflow must carefully be considered to fulfil a good indoor climate.
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