Energy and utilities

Data mapping

Entity

meter or asset

Group

feeder or zone

Profile

generic; configure time windows with verticals.register

Reference implementation

from datetime import datetime, timedelta
from vertiq import plugins, rules
from vertiq.mapping import Mapping, apply, repair
from vertiq.tasks import predict_event, predict_value

# rows: the source export as a list of dicts, e.g. list(csv.DictReader(open("export.csv")))
mapping = Mapping.parse({"entity": "Meter ID", "time": "Reading Date", "type": "Event Type", "value": "kWh", "group": "Feeder", "attrs": ["Tariff"], "date_format": "%Y-%m-%d"})
m, notes = repair(mapping, rows)      # validates column names, value types and date format against the rows
ds, problems = apply(m, rows)         # rows that fail to load are reported, not imputed

@plugins.feature("utility")
def recent_outages(entity, before, cut):   # receives only events recorded before the cutoff
    return {"outages_90": sum(1 for e in before if e.type == "outage"
                            and e.at >= cut - timedelta(days=90))}

task = predict_event(ds, target="outage", window_days=30, pack="utility")
print(task.status, task.report())     # "trained" only if the model outperformed the baseline on held-out entities
if task.status == "trained":
    print(task.score("MTR-40211"))  # probability with contributing factors

size = predict_value(ds, target="reading", window_days=30, pack="utility")
print(size.status, size.report())

flags = rules.run(ds, [
    rules.repeat(7, type="outage"),   # a second outage on the same meter within seven days
])
print(flags["summary"])

This code is executed by the test suite (tests/test_guides.py) against generated records with these column names.