Currently, gas furnaces are common heating systems in Europe. Due to the efforts for decarbonizing the complete energy sector, heat pumps should continuously replace existing gas furnaces. At the same time, the electrification of the heating sector represents a significant challenge for the power grids and their operators. Thus, new approaches are required to estimate the additional electricity demand to operate heat pumps. The electricity required by a heat pump to produce a given amount of heat depends on the Seasonal Performance Factor (SPF), which is hard to model in theory due to many influencing factors and hard to measure in reality as the heat produced by a heat pump is usually not measured. Therefore, we show in this paper that collected smart meter data forms an excellent data basis on building level for modeling heat demand and the SPF. We present a novel methodology to estimate the mean SPF based on an unpaired dataset of heat pump electricity and gas consumption data taken from buildings within the same city by comparing the distributions using the Jensen-Shannon Divergence (JSD). Based on a real-world dataset, we evaluate this novel method by predicting the electricity demand required if all gas furnaces in a city were replaced by heat pumps and briefly highlight possible use cases.

翻译：目前，燃气炉是欧洲常见的供暖系统。为实现整个能源行业的脱碳目标，热泵需逐步替代现有燃气炉。与此同时，供暖系统的电气化对电网及其运营商构成重大挑战。因此，需要新方法来估算运行热泵所需的额外电力需求。热泵产生给定热量所需的电力取决于季节性性能系数（SPF），由于影响因素众多，该系数在理论上难以建模，而现实中热泵产生的热量通常未被计量，因此难以实测。本文证明，收集的智能表数据为建筑层面热需求与SPF建模提供了优异的数据基础。我们提出一种基于非配对数据集的新方法——利用詹森-香农散度（JSD）比较同一城市内建筑的热泵用电与燃气消耗数据分布，从而估算平均SPF。基于真实数据集，通过预测该城市所有燃气炉被热泵替代后的电力需求，评估该新方法，并简要阐述可能的应用场景。