||view images for overall energy<br>efficiency<br>predictions<br>and<br>building attributes|-<br>Morphology<br>description<br>-<br>Google Street View<br>(GSV)||
|---|---|---|---|
|Fehrer and Krarti<br>(2018)|Use of nighttime light imagery<br>from VIIRS as a predictor of<br>building energy use intensity<br>within states, counties, and cities<br>in the United States|-<br>Satellite<br>data<br>from<br>VIIRS|US (several<br>levels)|
|Berger<br>and<br>Worlitschek<br>(2018)|Estimating<br>residential<br>space<br>heating demand via maps of<br>population density distribution<br>combined<br>with<br>norm<br>temperature profiles and the<br>definition of heatingdegree days|-<br>Population<br>-<br>Temperature<br>-<br>Energy<br>supply/consumption<br>-<br>Buildings|Switzerland|

_Source: Own elaboration based on literature review._

## **Big data sources might also help generate new insights into energy spending, consumption patterns,**

**and related social norms and behaviors.** For example, Fergus and Chalmers (2021) use data from smart meters, consumer access device (CAD) data, and machine learning algorithms to detect and monitor fuel poverty. They also generate new insights into energy consumption, behavior, social norms, and expectations of energy sobriety.

**Big data approaches are promising but require advanced data science skills and tools.** Big data approaches allow for real-time tracking and extremely granular measurement of energy poverty. At the same time, they require advanced data science skills and tools. In addition, they might raise concerns about data privacy, ethics, and security. Lastly, they might still abstract from the multidimensionality of energy poverty, especially considering quality, affordability, or differences in energy demands and needs.

# 2.8. Summary

**In summary, there are a variety of approaches to measuring energy poverty.** Each approach highlights different aspects of energy poverty, including strengths and weaknesses. Having these strengths and weaknesses in mind when designing operational measures of energy poverty and having clarity of what aspect of energy poverty one would want to measure can result in more effective and impactful measuring strategies. We summarize each of the measures and their respective strengths and weaknesses in Table 3.

_Table 3: Summary of energy poverty measures and strengths and weaknesses of each approach_

|**Name**|**Summary**|**Strengths**|**Weaknesses**|
|---|---|---|---|
|Access-based<br>measures|Lack of access to a<br>minimum level of energy<br>services,<br>such<br>as<br>electricity<br>and<br>clean<br>cooking fuels|Useful<br>to<br>improve<br>energy access; useful<br>to identify the most<br>deprived<br>population/region|Abstracts from quality and<br>reliability; does not factor<br>in variations in energy<br>needs and preferences;<br>does not account for<br>sustainability<br>and<br>affordability.|
|Expenditure-<br>based measures|Energy<br>poverty<br>is<br>spending more than a<br>certain percentage of<br>income or expenditure|Easy to calculate and<br>simple measure|Abstracts from quality and<br>quantity of energy services<br>and different household<br>realities;no consensus in|

20