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Artificial neural networks to predict energy performance and retrofit scenarios for any member of a building category: A novel approach

机译:人工神经网络预测建筑物类别任何成员的能源性能和改造方案:一种新颖的方法

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How to predict building energy performance with low computational times and good reliability? The study answers this question by employing artificial neural networks (ANNs) to assess energy consumption and occupants' thermal comfort for any member of a building category. Two families of ANNs are generated: the first one addresses the existing building stock (as is), the second one addresses the renovated stock in presence of energy retrofit measures (ERMs). The ANNs are generated in MATLAB (R) by using the outcomes of EnergyPlus simulations as targets for networks' training and testing. A preliminary 'Simulation-based Large-scale sensitivity/uncertainty Analysis of Building Energy performance' (SLABE) is conducted to optimize the ANNs' generation. It allows to identify the networks' inputs and to properly select the ERMs. The developed ANNs can replace standard building performance simulation tools, thereby producing a substantial reduction of computational efforts and times. This can allow a wide diffusion of rigorous approaches for retrofit design, which are currently hampered by the excessive computational burden. As case study, office buildings built in South Italy during 1920-1970 are investigated. Comparing the ANNs' predictions with EnergyPlus targets, the regression coefficient is between 0.960 and 0.995 and the average relative error is between 2.0% and 11%. (C) 2016 Elsevier Ltd. All rights reserved.
机译:如何以较低的计算时间和良好的可靠性来预测建筑物的能源性能?该研究通过使用人工神经网络(ANN)来评估建筑物类别中任何成员的能耗和居住者的热舒适性,从而回答了这个问题。生成了两类人工神经网络:第一个解决现有建筑资源(按现状),第二个解决存在能源改造措施(ERM)的翻新建筑。通过使用EnergyPlus仿真的结果作为网络训练和测试的目标,在MATLAB(R)中生成ANN。进行了初步的“基于模拟的建筑能效大型灵敏度/不确定性分析”(SLABE),以优化人工神经网络的生成。它允许识别网络的输入并正确选择ERM。开发的人工神经网络可以替代标准的建筑性能模拟工具,从而大大减少计算工作量和时间。这可以使严格的方法广泛地用于翻新设计,而这些方法目前受到过多的计算负担的困扰。作为案例研究,对1920-1970年在意大利南部建造的办公楼进行了调查。将人工神经网络的预测与EnergyPlus目标进行比较,回归系数在0.960至0.995之间,平均相对误差在2.0%至11%之间。 (C)2016 Elsevier Ltd.保留所有权利。

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