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Automated modelling of residential buildings and heating systems based on smart grid monitoring data

机译:基于智能电网监测数据的住宅建筑和加热系统自动建模

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摘要

More than 60% of the energy is consumed in European households for space heating. Combined with the currently low renovation rate, the realization of the 2000 W society will be challenging. A fast and remote detection of optimal retrofitting targets may help to address this problem. In this contribution, a novel procedure to characterise a building and its heating systems from smart grid monitoring data is presented. The parameters of a simplified physical simulation model for the building and heating system are adjusted to match the simulated and the actual power consumption of the heat pump. The method is validated on three reference buildings with almost perfect reproduction of the heat consumption over the course of one year and good recovery of relevant building/heating system parameters. The application on five real-world buildings shows the possibility to reproduce the annual heat consumption within a maximal deviation of 5% in four out of five cases. These convincing results enable also the application of the developed procedure as a load prediction tool. (C) 2020 The Authors. Published by Elsevier B.V.
机译:超过60%的能量在欧洲家庭中消耗用于空间加热。结合目前低改造率,实现2000年的社会将具有挑战性。快速和远程检测最佳改装目标可能有助于解决这个问题。在这一贡献中,提出了一种从智能电网监测数据中表征建筑物及其加热系统的新颖过程。调整了建筑和加热系统的简化物理仿真模型的参数,以匹配热泵的模拟和实际功耗。该方法在三栋参考建筑物上验证,在一年的一年内几乎完善的热量繁殖,以及相关建筑物/加热系统参数的良好恢复。五个现实建筑物的申请表明,在五种情况下,四种可能在最大偏差的最大偏差中繁殖年的热量消耗。这些令人信服的结果也可以将开发过程的应用作为负载预测工具。 (c)2020作者。 elsevier b.v出版。

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