SIMGIS : GIS FOR THERMAL SIMULATIONS IN THE BUILT ENVIRONMENT AUTHORS
Building simulation programs perform thermal simulations in a stand-alone scenario and, therefore, the resulting building automation is unable to handle a broad scenario of environmental changes. Due to the time needed for a building to react properly to short-term sudden weather changes, standard building automation is not enough to control energy consumption and thermal comfort efficiently: the sensors placed on the external envelop of a building provide information about weather changes too short in advance about weather changes. A meteorological station can inform in advance a group of buildings in an area about weather changes, but is unable, unlike a GIS, to identify the buildings which are really interested in the phenomenon and to send the weather changes information only to those buildings. The Swiss Federal Institute of Technology (ETH) has developed a new methodology to optimize building thermal performances by utilizing GIS, simulation, on line meteorological data and building automation.
Accounting free gains in a non-residential building by means of an optimal stochastic controller
Abstract A prototype predictive controller, based on the theory of optimal stochastic control, was developed and installed in a non-residential building in Delemont (Switzerland). This building is very well insulated (U = 0.65 W/m2K for windows) and, moreover, equipped with an active floor heating and cooling system. Solar and free gains supply more than 50% of its heating energy during winter. The controller determines, with the aid of the predicted free gains, the heat to supply for the next hour in order to optimise comfort and minimise energy consumption over a period of 24 h. The performance of this controller is compared with that of an advanced external temperature controller installed in the same building. A detailed energy balance of the building is presented for both controller types. The maximal energy savings observed in favour of the predictive controller amount to 24% during the cold season (October through April) and even reach 31% during the hot season (May through September). The thermal comfort was evaluated through questionnaires filled in by the occupants, but also thanks to an analysis based on temperature monitoring in the building. The analysis results show that comfort was maintained by the predictive controller.
Energy
Energy is often defined as the ability to do work. There are many forms of energy. A few examples include; Gravitational energy, Kinetic energy (energy of motion), Thermal energy (heat energy), Potential energy. Potential energy is the energy something has based on its position in the universe. A stretched elastic band has potential energy. A rock held up in the air has potential energy based on its position.