How to Use Python to Better Understand the Impact of Electricity Pricing on Consumption
summary
Processes the Low Carbon London trial with Python and pandas at half hour resolution across 167 million readings and builds a counterfactual estimator by mapping control-to-treatment behavior from 2012 to 2013 (dimensionality reduction to T vectors and a linear fit). The claim is that dynamic time‑of‑use pricing reduces consumption in high‑price intervals, with about 12% shedding for two socioeconomic groups and ~4.51% for the third, so the method produces a measurable effect.
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chapters (6)
- 0:00Electricity pricing basics
- 10:15Causal inference in pricing
- 20:30London smart meter data
- 28:45Mathematical consumption model
- 34:20Python for treatment effects
- 37:50Dynamic pricing results
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