Hourly gas usage was analyzed across three climate zones to help a utility prioritize decommissioning zones. The client lacked detailed data on variation by appliance type, housing stock, and weather. Smart meter data from 1.2 million households was processed and enriched with GIS-based building and income data.
The decision behind the question
A utility had to choose which climate zones to decommission first, but lacked detail on how gas use varied by appliance type, housing stock, and weather.
Data and methods used
Smart meter data from 1.2 million households was processed at hourly resolution across three climate zones and enriched with GIS building attributes and income data, then modelled to separate weather-driven load from structural load.
What the analysis found
- •Hourly load shapes differed sharply between the three zones even after weather was controlled for.
- •Housing stock age explained more of the remaining variation than appliance mix did.
- •One zone's peak demand was concentrated in a narrow window, which changed its decommissioning difficulty.
What changed as a result
Decommissioning sequencing was set using the zone-level load shapes and building attributes, replacing the utility's earlier reliance on aggregate annual volumes.
Related work is grouped under Climate & Energy, and the methods used here are described in more detail under analytical capabilities.
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