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Currently, gas furnaces are common heating systems in Europe. Due to theefforts for decarbonizing the complete energy sector, heat pumps shouldcontinuously replace existing gas furnaces. At the same time, theelectrification of the heating sector represents a significant challenge forthe power grids and their operators. Thus, new approaches are required toestimate the additional electricity demand to operate heat pumps. Theelectricity required by a heat pump to produce a given amount of heat dependson the Seasonal Performance Factor (SPF), which is hard to model in theory dueto many influencing factors and hard to measure in reality as the heat producedby a heat pump is usually not measured. Therefore, we show in this paper thatcollected smart meter data forms an excellent data basis on building level formodeling heat demand and the SPF. We present a novel methodology to estimatethe mean SPF based on an unpaired dataset of heat pump electricity and gasconsumption data taken from buildings within the same city by comparing thedistributions using the Jensen-Shannon Divergence (JSD). Based on a real-worlddataset, we evaluate this novel method by predicting the electricity demandrequired if all gas furnaces in a city were replaced by heat pumps and brieflyhighlight possible use cases.

Modeling of Annual and Daily Electricity Demand of Retrofitted Heat Pumps based on Gas Smart Meter Data