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首页> 外文会议>Second ICSC Symposium on Engineering of Intelligent Systems, Jun 27-30, 2000, Scotland, U.K. >INTELLIGENT MODELLING OF BUILDINGS BY SOFT-COMPUTING METHODS (extended abstract submitted for possible presentation at EIS'2000 as a regular paper)
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INTELLIGENT MODELLING OF BUILDINGS BY SOFT-COMPUTING METHODS (extended abstract submitted for possible presentation at EIS'2000 as a regular paper)

机译：通过软计算方法对建筑物进行智能建模（扩展摘要摘要提交给EIS'2000，可能作为常规论文发表）

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The paper presents recent results on the application of soft computing techniques for predictive modelling in the built sector. More specifically, an air-conditioned zone (Anglesea Building, University of Portsmouth), a naturally ventilated room (Portland Building, University of Portsmouth), and an ambient energy building (St Catherine's Lighthouse, Isle of Wight) are considered. The zones are subjected to occupancy effects and external disturbances which are difficult to predict in a quantitative way and hence the soft computing approach seems to be a better alternative. In fact, the overall complexity of the problem domain makes the modelling of the internal climate in buildings a difficult task which is not always carried out in a satisfactory way by traditional deterministic and stochastic methods. It seems reasonable to investigate the capabilities of the soft-computing approach as an alternative modelling strategy because of its heuristic nature and abilities to perform well in uncertain and/or incomplete information situations. In this respect, it is also interesting to see if it could offer good solutions for the built sector and this paper describes briefly the results from a detailed systematic study of three types of buildings. The soft-computing approach presented uses fuzzy logic for modelling, as well as neural networks and genetic algorithms for adaptation and optimisation of the fuzzy model. The latter is of the Takagi-Sugeno type and it is built by subtractive clustering as a result of which the initial values of the antecedent non-linear membership functions and the consequent linear algebraic equation parameters are determined. In order to obtain a better fit to the measured data, these parameters are further adjusted by back-propagation neural networks and real-valued genetic algorithms. Modelling results with actual building data are presented in Figures 1-6 where the initial (fuzzy) and the final (fuzzy-neuro / fuzzy-genetic) models are shown.
机译：本文介绍了有关软计算技术在建筑行业预测模型中的应用的最新结果。更具体地说，考虑了一个空调区域（朴次茅斯大学的安格西大厦），自然通风的房间（朴次茅斯大学的波特兰大厦）和周围的能源建筑（怀特岛的圣凯瑟琳灯塔）。这些区域受到占用效应和外部干扰的影响，很难以定量的方式进行预测，因此软计算方法似乎是更好的选择。实际上，问题域的整体复杂性使建筑物内部气候的建模成为一项艰巨的任务，而这通常不能通过传统的确定性和随机方法以令人满意的方式进行。研究软计算方法作为替代建模策略的能力似乎是合理的，因为它具有启发性，并且具有在不确定和/或不完整的信息情况下表现良好的能力。在这方面，还很有趣的是，它能否为建筑行业提供良好的解决方案，本文简要介绍了对三种类型建筑物的详细系统研究得出的结果。提出的软计算方法使用模糊逻辑进行建模，并使用神经网络和遗传算法进行模糊模型的适应和优化。后者是Takagi-Sugeno类型的，它是通过减法聚类构建的，其结果是确定了先前的非线性隶属函数的初始值以及相应的线性代数方程参数。为了获得对测量数据的更好拟合，这些参数将通过反向传播神经网络和实值遗传算法进行进一步调整。图1-6显示了具有实际建筑数据的建模结果，其中显示了初始（模糊）模型和最终（模糊神经/模糊遗传）模型。

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