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Detection of heat pumps from smart meter and open data

机译：从智能仪表和打开数据检测热泵

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Heat pumps embody solutions that heat or cool buildings effectively and sustainably, with zero emissions at the place of installation. As they pose significant load on the power grid, knowledge on their existence is crucial for grid operators, e.g., to forecast load and to plan grid operation. Further details, like the thermal reservoir (ground or air source) or the age of a heat pump installation renders energy-related services possible that utility companies can offer in the future (e.g., detecting wrongly calibrated installations, household energy efficiency checks). This study investigates the prediction of heat pump installations, their thermal reservoir and age. For this, we obtained a dataset with 397 households in Switzerland, all equipped with smart meters, collected ground truth data on installed heat pumps and enriched this data with weather data and geographical information. Our investigation replicates the state of the art in the area of heat pump detection and goes beyond it, as we obtain three major findings: First, machine learning can detect the existence of heat pumps with an AUC performance metric of 0.82, their heat reservoir with an AUC of 0.86, and their age with an AUC of 0.73. Second, heat pump existence can be better detected using data during the heating period than during summer. Third the number of training samples to detect the existence of heat pumps must not be necessarily large in terms of the number of training instances and observation period.
机译：热泵有效和可持续地体现了加热或凉爽的建筑物，在安装地点提供零排放。随着它们对电网的显着负载构成了大量负载，对其存在的了解对于电网运营商，例如，预测负载和计划网格操作是至关重要的。进一步的细节，如热储存器（地面或空气源）或热泵安装的年龄，可能与能源相关的服务可能在未来提供（例如，检测错误校准的安装，家庭能效检查）。本研究调查了热泵装置，其热水库和年龄的预测。为此，我们在瑞士拥有397户家庭的数据集，都配备了智能电表，收集了安装热泵的地面真理数据，并通过天气数据和地理信息丰富了此数据。我们的调查在热泵检测领域复制了现有技术，超出了它，如我们获得的三个主要结果：首先，机器学习可以检测到0.82的AUC性能度量的热泵存在，它们的热藏AUC的0.86，他们的年龄为0.73。其次，可以在加热期间比夏季期间使用数据更好地检测热泵存在。第三次检测热泵存在的训练样本的数量在训练实例和观察期的数量方面不一定不一定。

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