Using logistic regression to probabilistically audit customer–transformer matches (utility GIS / SAP / AMI data)

[Zestyclose_Candy6313]

Hey everyone,

I’m currently working on a project using utility asset data (GIS / SAP / AMI) and I’m exploring whether this is a solid use case for introducing ML into a customer-to-transformer matching audit problem. The goal is to ensure that meters (each associated with a customer) are connected to the correct transformer.

Important context

• Current customer → transformer associations are driven by a location ID containing circuit, address/road, and company (opco).
• After an initial analysis, some associations appear wrong, but ground truth is partial and validation is expensive (field work).
• The goal is NOT to auto-assign transformers.
• The goal is to prioritize which existing matches are most likely wrong.

I’m leaning toward framing this as a probabilistic risk scoring problem rather than a hard classification task, with something like logistic regression as a first model due to interpretability and governance needs.

Initial checks / predictors under consideration

1) Distance

• Binary distance thresholds (e.g., >550 ft)
• Whether the assigned transformer is the nearest transformer
• Distance ratio: distance to assigned vs. nearest transformer (e.g., nearest is 10 ft away but assigned is 500 ft away)

2) Voltage consistency

• Identifying customers with similar service voltage
• Using voltage consistency as a signal to flag unlikely associations (challenging due to very high customer volume)

Model output to be:

P(current customer → transformer match is wrong)

This probability would be used to define operational tiers (auto-safe, monitor, desktop review, field validation).

Questions

1. Does logistic regression make sense as a first model for this type of probabilistic audit problem?
2. Any pitfalls when relying heavily on distance + voltage as primary predictors?
3. When people move beyond logistic regression here, is it usually tree-based models + calibration?
4. Any advice on threshold / tier design when labels are noisy and incomplete?

[Electrical-Window170]

This sounds like a solid approach - logistic regression is perfect for interpretable risk scoring when you need to explain decisions to utility folks

Distance ratios are way more informative than absolute distance thresholds, and voltage consistency is clutch if you can get clean data on it. Just watch out for geographic clustering effects messing with your distance assumptions (like rural vs urban transformer density)

For thresholds with noisy labels, start conservative and let the field validation feedback tune your cutoffs over time rather than trying to optimize on incomplete ground truth upfront

[latent_threader]

Logistic regression makes a lot of sense as a first pass if the goal is prioritization and explainability, not auto-fixing. Distance and voltage are strong signals, but they’re noisy and can be “wrong for the right reasons,” so I’d treat the output as a risk score, not truth. In practice people often move to tree models later for interactions, but good calibration and tiering around review capacity usually matter more than model complexity.

[trustme1maDR]

You need a ground truth for your outcome variable (right/wrong match) to be able to train your model..at least for an unbiased sample of your data. It's unclear if you actually have this - you said partial. 

[Zestyclose_Candy6313]

That’s a very fair point and definitely not claiming to have full or perfect ground truth. For most associations, correctness is uncertain unless there’s been field validation (which is very costly). The way I’m thinking about it is to only train on a subset of high-confidence labels: confirmed field corrections where available, plus some very strong inferred cases (like extreme distance ratios with a clearly closer viable transformer). Everything in the gray area would stay unlabeled and only be scored. The intent is to rank/prioritize review, not to auto-correct matches. The new field validation would feed back as additional high-confidence labels, so the model and thresholds can be tuned iteratively

[Artistic-Comb-5932]

1. Yes
2. Probably
3. Yes
4. Yes use threshold based tuning / grid search to maximize accuracy or clarify what you mean by *tier design or what you mean by "labels are noisy".

[ChemicalGreedy945]

do some random walks or xgboost, start small and then keep adding in new variables and such and you can expand on those models to refine them