Multi vector energy demand modelling for predicting low-carbon heat loads

The push to decarbonize heating through the adoption of low-carbon heating alternatives, such as electrical heat pumps, will alter network load magnitudes and shapes at the LV power distribution network level. Due to the lack of monitoring at the distribution level it is of interest to develop methods to infer LV network conditions in the absence of complete data. Limited present uptake of domestic heat pumps in the UK limits available data to use for localized predictions sensitive to household specific time of use and magnitude variability. This work demonstrates a methodology for inferring potential future electrical heat load from existing household electrical and gas demand data, facilitating the prediction of future electrical heat load from limited data. Recurring load profiles from gas and electrical data from the Energy Demand Research Project are identified and clustered using a k-means clustering approach. The relation of these recurring load profiles with respect to each other is mapped using a through the construction of a Markov model with transition probabilities trained from household electrical and gas demand data. The use of this approach to infer future electrical heat load is then demonstrated in a simple case study.