One size does not fit all: Averaged data on household electricity is inadequate for residential energy policy and decisions

• We create a model to predict the significant appliances that are found in a home.
• The model adjusts RECS data based on penetration rates in different census regions.
• The model is also applied to homes grouped by size, income, and electricity use.
• We show national data is not suitable for some household level policy decisions.
• We show which appliances are expected to be significant under various scenarios.

Residential electricity users need more detail than monthly bills to reduce consumption. With the emergence of technologies that provide detailed usage estimates for energy consumption, two questions arise. First, how many different energy-consuming appliances contribute to household electricity load, and secondly which appliances? Using national average penetration rates, the Residential Energy Consumption Survey (RECS), estimates that 42 unique appliances account for 93% of electricity consumption, while 12 appliances account for 80% of average household electric load. A typical scenario is developed from national and regional penetration rates and find that eight appliances are responsible for 80% of a household's electric load in the United States. Four household scenarios are developed: a house that uses electric appliances, gas appliances, the average household, and typical household. It is concluded that RECS cannot be used as a representative household as it overestimates the number of appliances that contribute to a household electric load. The number of significant appliances is affected by appliance ownership and use, which is more variable between homes than between census divisions. These results can be used to design and maximize the value of residential energy information and management systems.