Smart technologies play a vital role in facilitating the response of buildings to the external conditions, including climate, grid, and the internal building requirements such as user needs. A salient concern lingers in relation to existing buildings due to their increasing energy consumption. Around 35 % of EU buildings are older than 50 years and 90 % are built before the nineties. In this sense, smart retrofitting represents a key step towards achieving energy-responsive flexible buildings. Quantifying building energy performance with appropriate Key Performance Indicators is a critical step towards achieving decarbonization goals in both existing and new buildings. In this paper a group of five representative indicators has been selected to measure the energy performance of smart features in retrofitted and new buildings, also identifying distinct performance thresholds. Therefore, each threshold defines minimum acceptable and top performing values for the indicators. Accordingly, thresholds are set first based on previous literature and performance data, then a Logical Evaluation Methodology is used to identify suitable range of thresholds. Results of this paper propose a quantified definition for smart retrofitting, which involves transforming an existing building into a Smart Building. A smart building is a nearly Zero Energy Building that achieves primary energy savings of 30 % to 80 % and can adapt to changing climate and grid conditions. It should communicate with users and predict operational failures using a Building Energy Management System. Additionally, it should enable load shifting in response to renewable energy source production and electricity prices by 30 % to 70 % annually, while minimizing grid interaction to 10 % to 30 % on an hourly basis throughout the year. Furthermore, it should allow for RES self-consumption of 30 % to 70 % and cover 20 % to 70 % of the load with RES annually. Finally, the indicators are tested on a case study in Italy within a Horizon 2020 project to validate the thresholds.

中文翻译：

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通过关键绩效指标评估智能建筑和智能改造干预措施的性能：定义最低性能阈值


智能技术在促进建筑物对外部条件（包括气候、电网和内部建筑要求，如用户需求）的响应方面发挥着至关重要的作用。由于现有建筑的能源消耗不断增加，因此存在一个突出的问题。大约 35% 的欧盟建筑楼龄超过 50 年，90% 建于 90 年代之前。从这个意义上说，智能改造是实现能源响应型柔性建筑的关键一步。使用适当的关键绩效指标量化建筑能源绩效是实现现有和新建筑脱碳目标的关键一步。在本文中，选择了一组五个具有代表性的指标来衡量改造和新建建筑中智能功能的能源性能，并确定了不同的性能阈值。因此，每个阈值都定义了指标的最小可接受值和最高绩效值。因此，首先根据以前的文献和性能数据设置阈值，然后使用逻辑评估方法来确定合适的阈值范围。本文的结果提出了智能改造的量化定义，其中包括将现有建筑转变为智能建筑。智能建筑是一种近乎零能耗的建筑，可实现 30% 到 80% 的一次节能，并且可以适应不断变化的气候和电网条件。它应该与用户沟通并使用建筑能源管理系统预测运营故障。 此外，它应能够根据可再生能源生产和电价每年将负载转移 30% 至 70%，同时将全年每小时的电网互动减少到 10% 至 30%。此外，它应允许 30 % 至 70 % 的 RES 自身消耗，并每年用 RES 覆盖 20 % 至 70 % 的负载。最后，在意大利的 Horizon 2020 项目中的案例研究中对指标进行了测试，以验证阈值。