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

TL;DR

This paper introduces a novel method to estimate heat pump electricity demand using smart meter data, aiding in understanding grid impacts of replacing gas furnaces with heat pumps in urban areas.

Contribution

It presents a new approach to model heat pump energy use and Seasonal Performance Factor from building-level smart meter data using Jensen-Shannon Divergence.

Findings

Effective estimation of heat pump electricity demand from smart meter data

Potential to predict city-wide energy impacts of heat pump adoption

Method demonstrated on real-world dataset

Abstract

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.