The accurate simulation of energy performance is fundamental to design low energy buildings – newly-built or after refurbishment – and thus to promote sustainability. However, this is an involved task requiring high complexity in modeling and simulation. Most accurate building energy simulation tools are not user-friendly for building professionals, thereby hindering a widespread use of effective methodologies – developed by researchers – for the transition to nearly- or net-zero energy buildings. This study tries to solve such an issue by proposing a novel, accurate but user-friendly tool for building modeling and energy simulation. The tool is denoted as EMAR because it is based on the advanced coupling between ENergyplus and MAtlab® addressing Residential buildings, which are a major part of the existing stocks. EMAR completely works under MATLAB® environment and only needs around 60 numerical inputs to generate simplified building models and perform accurate energy simulations. The available outputs are numerous referring to energy and economic performance as well as thermal comfort. No drawings, no schemes of energy systems, no deep modeling expertise are required but only few numbers. EMAR is validated against detailed EnergyPlus models of an ASHRAE test building and of two typical European buildings. The discrepancies are lower than 10% as concerns thermal and primary energy needs, and in most cases are lower than 5%, but the modeling complexity and computational burden are drastically reduced. Thus, EMAR can be a precious tool to perform user-friendly but accurate building energy modeling and simulations.

准确模拟能源性能对于设计新建或翻新的低能耗建筑至关重要，因此可以促进可持续性。但是，这是一项涉及的任务，需要在建模和仿真中具有很高的复杂性。对于建筑专业人士而言，大多数准确的建筑能源模拟工具对用户并不友好，从而阻碍了由研究人员开发的有效方法的广泛使用，这些方法已过渡到接近零或净零的能源建筑。这项研究试图通过提出一种新颖，准确，但用户友好的建筑建模和能源模拟工具来解决这一问题。该工具被称为EMAR，因为它基于ENergyplus之间的高级耦合和MAtlab®解决住宅建筑问题，这是现有库存的主要部分。EMAR完全在MATLAB®环境下工作，仅需要大约60个数字输入即可生成简化的建筑模型并执行准确的能源模拟。可用输出涉及能源和经济性能以及热舒适性。不需要图纸，不需要能源系统方案，不需要深入的建模专业知识，但数量很少。EMAR已针对ASHRAE测试建筑物和两座典型欧洲建筑物的详细EnergyPlus模型进行了验证。考虑到热能和一次能源的需求，差异低于10％，并且在大多数情况下低于5％，但是建模复杂性和计算负担却大大降低了。因此，