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气象参数是建筑能耗模拟的基础,随着全球气候异常变暖,必将对建筑采暖和空调能耗产生重要影响。进行未来气候条件下的建筑能耗模拟,必须首先开展未来模拟气象参数的研究。根据TMY2模拟气象参数模式提出了节能分析气象年(AEEMY)模拟气象参数模式。使用了3个气候模型预测了中国建筑热工分区代表城市未来2021-2050的30 a气象参数。使用AEEMY模式得到了1971-2000年和2021-2050年代表城市的建筑能耗模拟气象参数。应用DOE2模拟软件对中国各气候区的居住建筑在2种气候条件下进行了建筑能耗模拟。验 相似文献
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气象参数是影响建筑热环境和供暖空调能耗的主要因素之一。基于成都地区1971—2000年共30a的历史观测数据,生成了建筑能耗模拟软件EnergyPlus所需要的逐时气象数据文件。比较分析了该地区30a干球温度、太阳辐射等各气象参数月均值的变化,模拟分析了该地区建筑的采暖、制冷及总能耗,利用多元回归建立了建筑能耗与气象参数之间的关系式,并检验了该关系式的准确性。结果表明:成都地区办公建筑能耗变化与各气象参数没有呈现明显的规律性;建筑月总能耗与各气象参数呈纯二次多项式关系,月采暖能耗、月制冷能耗与各气象参数呈交叉二项式关系;建筑月能耗回归模型能够较准确地预测建筑月能耗与各气象参数的关系,且月采暖能耗和月制冷能耗回归模型预测的准确性优于月总能耗模型。 相似文献
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居住建筑能耗预测分析方法的研究 总被引:2,自引:0,他引:2
本文通过正交实验设计、DeST建筑能耗软件模拟及多元线性回归方法,研究了影响居住建筑能耗的主要建筑设计因素,得到了重庆市居住建筑冷、热负荷及全年负荷的预测方程,并进行了误差分析。通过预测方程和模拟软件分别计算所得结果的对比分析,验证了该方程的可靠性,为重庆市居住建筑能耗预测分析提供了一种简便适用的方法。利用可靠的预测方程,可以快速而简单地评价分析不同设计方法和围护结构方式对建筑能耗的影响。 相似文献
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改进BP神经网络在软土地基沉降量中的应用 总被引:1,自引:0,他引:1
利用神经网络强大的非线性映射能力,提出了一种基于BP神经网络模型的软土地基沉降量的预测方法,对不同情况下软土路基沉降量进行合理地预测,实例检验证明,该方法收敛速度快,预测的可靠性高。 相似文献
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周志豪 《建筑热能通风空调》2020,39(3):1-7,15
为优化中央空调冷源系统运行能耗,本文分别建立了中央空调冷源系统运行能耗预测灰箱模型和BP神经网络模型,对比分析了灰箱模型与BP神经网络模型的能耗预测性能,并基于K-means聚类算法提出了将灰箱模型和BP神经网络模型相结合的能耗预测混合模型。结合中央空调冷源系统能耗预测混合模型,以模型可控输入变量为优化变量,对中央空调冷源系统进行节能优化。结果表明:对比单独使用灰箱模型或BP神经网络模型,中央空调冷源系统混合模型能耗预测精度提升了27.7%和33.85%。对比冷源系统优化前能耗,优化后的中央空调冷源系统运行能耗平均降低了8.2%。 相似文献
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A.A. Javadi 《Tunnelling and Underground Space Technology incorporating Trenchless Technology Research》2006,21(1):9-20
This paper explores the capabilities of neural networks to predict the air losses in compressed air tunneling. Field data from the Feldmoching tunnel in Munich were used in this study. In this project, compressed air was used to retain the groundwater and shotcrete was used as temporary support. The final permanent lining was installed in free air. The tunnel passed through variable ground conditions ranging from coarse gravel to sand and clay. Grouting, an additional layer of shotcrete and a layer of mortar were occasionally used to control the air losses. A back-propagation feed forward neural network was trained and used to predict the air losses from the Feldmoching tunnel. The results of the prediction of the air losses from the tunnel using a neural network were compared with the field measurements. Data from different tunnel lengths were used for training. In each case, the trained network was used to predict the air losses during the excavation of the rest of the tunnel. It is shown that, not only can a neural network learn the relationship between appropriate soil and tunnel parameters and air losses, it can also generalize the learning to predict air losses for very different geological and geometric conditions. It is also shown that data from a very short length (50 m in one case) of the tunnel (five data point only, in this case) may contain enough information for the neural network to learn and predict the air losses in the remaining (585 m) length of the tunnel with a good degree of accuracy. This can be of considerable value to tunnel engineers in control of tunneling operations and help them in preparation for possible changes in air losses with tunnel advance, with changes in ground conditions and tunnel geometry and with time. 相似文献
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通过对火灾预测的发展过程和火灾预测可行性的讨论,介绍了火灾预测的基本原理,分析了定性预测法、灰色预测法、人工神经网络预测法、数理统计法、回归预测法等在火灾预测和统计中的应用。 相似文献
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利用Gompertz曲线模型、灰色理论模型和BP神经网络模型等单一预测模型对基坑周围建筑物沉降进行预测,之后将结果与三者优化组合模型的预测结果进行比较分析,结果表明:单一模型预测结果的精度比三者优化组合模型的精度较低,而其中通过最优加权法组合的模型预测精度最高。 相似文献
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Most building energy simulations tend to neglect microclimates in building and system design, concentrating instead on building
and system efficiency. Energy simulations utilize various outdoor variables from weather data, typically from the average
weather record of the nearest weather station that is located in an open field, near airports and parks. The weather data
may not accurately represent the physical microclimate of the site, and may therefore reduce the accuracy of simulation results.
For this reason, this paper investigates utilizing computational fluid dynamics (CFD) with neural network (NN) model to predict
site-specific wind parameters for energy simulation. The CFD simulation is used to find selected samples of site-specific
wind conditions. Findings from CFD simulation are used as training data for NN. A trained NN predicts site-specific hourly
wind conditions for a typical year. The outcome of the site-specific wind condition from the neural network is used as wind
condition input for the energy simulation. The results of energy simulation using typical weather station data and site-specific
weather data are compared in this paper, in order to find the possibility of using site-specific weather condition by NN with
CFD to yield more realistic and robust ES results. 相似文献
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总结分析了一般混凝土结构剩余使用寿命预测方法,在吸取等维灰色递补 GM(1,1)模型的优点之后,提出了利用 RBF 神经网络对既有钢筋混凝土结构的剩余使用寿命进行预测的方法。通过理论分析及应用,证明了其可行性。 相似文献
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为解决城市用水量预测中单一方法预测精度不高的问题,建立了灰色径向基(RBF)神经网络组合模型。对比实验结果表明,灰色GM(1,1)模型、RBF神经网络模型和灰色RBF神经网络组合模型的平均相对误差分别为2.1222%,1.2562%和0.6821%。与灰色GM(1,1)模型和RBF神经网络相比,灰色RBF神经网络组合模型充分发挥了灰色系统的贫乏数据建模和RBF神经网络的高度非线性映射能力的双重优势,具有较高的预测精度,更适合用于城市用水量预测。 相似文献