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Segmentation of household smart meter data linked to PV and battery value
  • Edward Barbour
Edward Barbour
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The primary challenge with big smart meter data is to gain actionable insights which ultimately enhance the sustainability of the power network. In this paper, we analyze 1 year of smart meter data from 326 households in Austin, TX. Via clustering, we find distinct daily usage patterns, illustrating heterogeneous consumption behavior which changes throughout the year. However, most consumers have at least 56% of days explained by 3 out of 24 identified load types. For each load type we estimate the value of PV and batteries under flat rate and Time-Of-Use electricity tariffs. We find that knowledge of a consumers most common load shapes can significantly improve estimates for PV viability compared to a control estimate based on population-wide averages. However, knowledge of the most common load shapes does not improve estimates of battery viability unless electricity prices are time-varying. This highlights that, in general, the information contained by load shape clusters is of high value when consumers face economic choices that depend on the timing of their consumption. This work builds on current knowledge by explicitly linking smart meter segmentation techniques to individual consumer suitability for different distributed energy technologies.