首页> 外文会议>International Conference on Advances in Building Technology Vol.2, Dec 4-6, 2002, Hong Kong, China >BUILDING HVAC SYSTEMS ECONOMIC PERFORMANCE EVALUATION USING NEURAL NETWORK METHOD AT BEGINNING OF DESIGN
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BUILDING HVAC SYSTEMS ECONOMIC PERFORMANCE EVALUATION USING NEURAL NETWORK METHOD AT BEGINNING OF DESIGN

机译:在设计开始时使用神经网络方法建立HVAC系统的经济绩效评估

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摘要

Two aspects need to be done to assess the performance of building Heating Ventilation and Air Conditioning (HVAC) systems. One is to evaluate the extent to which an HVAC scheme satisfies the air conditioning requirement. The other is to evaluate the economic characteristics of several HVAC schemes, all of which can satisfy the air conditioning demand. It is difficult to evalua te the economic performance of HVAC systems because economic evaluation involves some factors that frequently change and often unknown such as equipment price, especially at the beginning of the design stage. Furthermore, there is a conflict between the situation of knowing few information of HVAC system and the demand of detailed information to evaluate economic characteristics at design beginning stage. Neural network method can solve these problems for its self-adaptive ability and convenient multi-variable input-output function. ADLINE neural network model is analyzed and used to evaluate the economic characteristics of HVAC systems. The compare between the value predicted by ADLINE and the value calculated in detail shows that the relative error is less than 6% and the economic performance evaluation using ADLINE neural network model can satisfy the demand of actual HVAC project design.
机译:需要评估两个方面,以评估建筑物的采暖通风和空调(HVAC)系统的性能。一种是评估HVAC方案满足空调要求的程度。另一个是评估几种HVAC方案的经济特性,所有方案都可以满足空调需求。评估HVAC系统的经济性能非常困难,因为经济评估涉及一些经常变化且常常未知的因素,例如设备价格,尤其是在设计阶段的开始。此外,在设计开始阶段,对HVAC系统了解很少的情况和对详细信息进行评估以评估经济特征的需求之间存在冲突。神经网络方法以其自适应能力和便捷的多变量输入输出功能可以解决这些问题。分析了ADLINE神经网络模型并将其用于评估HVAC系统的经济特性。 ADLINE预测值与详细计算值的比较表明,相对误差小于6%,使用ADLINE神经网络模型进行经济绩效评估可以满足实际HVAC项目设计的需求。

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