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CASA, cost-optimal analysis by multi-objective optimisation and artificial neural networks: A new framework for the robust assessment of cost-optimal energy retrofit, feasible for any building

机译：CASA，通过多目标优化和人工神经网络进行的成本优化分析：一种用于对成本优化的能源改造进行稳健评估的新框架，适用于任何建筑物

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摘要

The cost-optimal analysis provides the most cost-effective energy retrofit solutions. This can strongly support the deep renovation of buildings. However, an outstanding question arises: How to achieve the robust assessment of cost-optimal solutions, feasible for any building? The paper answers this question by proposing a new multi-stage framework for cost-optimal analysis by multi-objective optimisation and artificial neural networks, called CASA. It couples EnergyPlus and MATLAB. A genetic algorithm allows to select recommended retrofit packages by minimizing energy consumption and thermal discomfort. Among these packages, the cost-optimal solution is identified. It is robust because the algorithm explores a wide domain of retrofit scenarios. The optimization procedure uses artificial neural networks to predict building performance. Large-scale uncertainty and sensitivity analyses are conducted to support the generation of the networks. These latter are tested against data provided by current literature with excellent results. The networks' applicability to whole building categories and rapidity of evaluation make the procedure feasible for any building. For demonstration, CASA is applied to a reference office building located in South Italy, by investigating the related category. The achieved cost-optimal solution produces global cost savings around 42.4(sic)/m(2), and significant reductions of energy consumption, discomfort hours and polluting emissions. 2017 Elsevier B.V. All rights reserved.
机译：成本优化分析提供了最具成本效益的能源改造解决方案。这可以有力地支持建筑物的深度翻新。但是，出现了一个悬而未决的问题：如何对任何建筑物都可行的成本最优解决方案进行可靠的评估？本文提出了一种新的多阶段框架，用于通过多目标优化和人工神经网络进行成本优化分析，该框架称为CASA。它结合了EnergyPlus和MATLAB。遗传算法可以通过最小化能耗和热不适来选择推荐的改装套件。在这些软件包中，确定了成本最优的解决方案。它是稳健的，因为该算法探索了广泛的改造方案。优化过程使用人工神经网络来预测建筑性能。进行了大规模不确定性和敏感性分析，以支持网络的生成。后者根据最新文献提供的数据进行了测试，结果非常出色。网络对于整个建筑物类别的适用性和评估的快速性使得该程序对于任何建筑物都是可行的。为了进行演示，通过调查相关类别，将CASA应用于位于意大利南部的参考办公大楼。所实现的成本最优解决方案可节省约42.4（sic）/ m（2）的全球成本，并显着降低能耗，不舒适时间和污染排放。 2017 Elsevier B.V.保留所有权利。

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