首页> 外文会议>ASME international mechanical engineering congress and exposition >A CONVENIENT LOW ORDER THERMAL MODEL FOR HEAT TRANSFER CHARACTERISTICS OF SINGLE FLOORED LOW-RISE RESIDENTIAL BUILDINGS
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A CONVENIENT LOW ORDER THERMAL MODEL FOR HEAT TRANSFER CHARACTERISTICS OF SINGLE FLOORED LOW-RISE RESIDENTIAL BUILDINGS

机译:单层低层住宅楼房传热特性的简便低阶热模型

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A low order thermal model is introduced to determine the thermal characteristics of a Low-Rise Residential (LRR) building and then predict the energy usage by its Heating Ventilation & Air Conditioning (HVAC) system according to future weather conditions. The LRR buildings are treated as a simple lump and the model is derived using the lumped capacitance model for transient heat transfer from bodies. Most contemporary HVAC systems have a thermostat control, which has an offset temperature, and user defined set point temperatures, which defines when the HVAC system will switch on and off. The aim is to predict, with minimal error, the inside air temperature, which is used to determine the switching on and off, of the HVAC system. To validate this lumped capacitance model we have used the EnergyPlus simulation engine, which simulates the thermal behavior of buildings with considerable accuracy. We have predicted using the low order model the inside air temperature of a single family house located in three different climate zones (Detroit, Raleigh & Austin) and different orientations for summer and winter seasons. The prediction error between the model and EnergyPlus is less than 10% for almost all the cases with the exception of Austin in summer. Possible factors responsible for error in prediction are also noted in this work, paving way for future research.
机译:引入低阶热模型来确定低层住宅(LRR)建筑物的热特性,然后根据未来的天气状况通过其采暖通风和空调(HVAC)系统预测能耗。 LRR建筑物被视为简单的块,并使用集总电容模型导出模型,以用于从车身进行瞬时传热。大多数现代HVAC系统都具有一个恒温器控件,该控件具有一个补偿温度,以及用户定义的设定点温度,该温度定义了何时打开和关闭HVAC系统。目的是以最小的误差预测用于确定HVAC系统开启和关闭的内部空气温度。为了验证该集总电容模型,我们使用了EnergyPlus模拟引擎,该引擎以相当高的精度模拟建筑物的热行为。我们已经使用低阶模型预测了位于三个不同气候带(底特律,罗利和奥斯丁)以及夏季和冬季的不同方向的单个房屋的室内空气温度。除了夏季的奥斯汀以外,几乎所有情况下,模型与EnergyPlus之间的预测误差均小于10%。这项工作中还指出了可能导致预测错误的因素,为将来的研究铺平了道路。

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