This paper proposed two simulation methods of the “Cooperation control “. The Cooperation control is defined the optimize connecting control in the HVAC, artificial lighting and day-lighting combined with venetian blind control. The first method is RBFN(Radial Basis Function Networks)[1] that is a kind of Neural Networks. The method is adopted for learning the data of the “Expert“ contorol value. The method makes the modl. The second is SOM (Self-organizing Maps) [2] that is another Neural Networks, too. SOM is one method of the Data Mining.
The paper presents a dynamic approach of the validation process of existing building simulation model. The normalization of the simulation results and the division of the electricity and gas bill are realized by taking into account the balance point temperatures for appropriate degree-days. These temperatures, as it was shown in this paper, could change during the validation process, as opposed to most cases where these temperatures are arbitrary fixed for the whole process.
A key barrier to the acceptance of simulation within building design has been identified as the fact that it is not fully integrated into the design process. The project described in this paper attempts to address this barrier by embedding modelling as a standard component of design practice procedures within an architectural practice. Important elements of the research that are described in the paper are:
To meet the requirements of building owners and users in environmental issues, life cycle assessment (LCA) has become an important method to monitor the ecological perspective during design and decision making processes. Existing LCA tools are separately available, but are missing the important links to other design tools, such as architectural, technical system design and energy simulation softwares. This paper presents a new integrated tool BSLCA for ecological design and analysis of building services.
Energy demand of different glazing materials and light control systems for office spaces are analyzed in this paper. The approach is based on an improvement of the hourly simulation program IENUS (Integrated ENergy Use Simulation), that was developed to assess building energy demand taking into account the integration between visual and thermal aspects. IENUS implements thermal aspects by the transfer function method, while daylighting is pre-processed by the package Superlite before entering in IENUS.
This paper presents the development of a zone model adapted to the study of the influence of the sensor position in building thermal control. The temperature measured by the sensor of a room temperature controller depends on its position in the zone. The measured sensor temperature depends of the convective coupling of the zone and its emitter and can differ from the “mean air temperature”.
In this paper, the impact of a veranda (attached sunspace) on energy consumption is analyzed by numerical simulations (Clim2000 software) for two different configurations : a (existing) residential individual house and a (new) apartment building.
We have developed software applications that design HVAC systems (that is, select, locate, and size components and their interconnections) given building descriptions, user preferences, and built-in rules.
The HVAC BESTEST has been developed within the framework of the IEA (International Energy Agency) It is a comparative test method developed for estimating the ability of whole building simulation programs to model the performance of unitary space cooling equipment.
The potential offered by computer simulation is often not realized: Due to the interaction of system vari- ables, simulation users rarely know how to choose input parameter settings that lead to optimal perfor- mance of a given system. Thus, a program called GenOpt® that automatically determines optimal pa- rameter settings has been developed. GenOpt is a generic optimization program. It min- imizes an objective function with respect to multi- ple parameters. The objective function is evaluated by a simulation program that is iteratively called by GenOpt.
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