What Load Is the Steel Plant Running?

Is the plant running a light, medium or maximum load right now? Knowing the load from the meters alone lets an energy manager shift heavy work to cheaper hours and spot waste without walking the floor.

Use caseEnergyPredictionModel evaluation

The table

Readings from a steel plant every 15 minutes for a year: energy used, reactive power, power factor, CO2 and the time of day, with the load type recorded for each reading. It's in Welt's research catalogue as Steel Industry Energy Consumption, 35,040 rows by 10 columns, licensed CC BY 4.0. The catalogue measures its signal at 0.76. At 35,040 rows and 9 features the table is larger than the model limits allow, so the walkthrough evaluates and fits on a seeded sample of 8,000 rows. TabICLv2 and Kumo Tabular Medium compete.

Walkthrough

Load it from the catalogue

Find the table, read its recorded target and print the column names.

import pandas as pd
from welt import Client

with Client() as client:
    item = client.research_datasets(q="steel industry energy consumption", limit=1)["items"][0]
    info = client.research_dataset(item["id"])
    data = client.load_research_dataset(item["id"])

target = info["targets"][0]
target = target["name"] if isinstance(target, dict) else target
print([column["name"] for column in info["schema"]], target)

Pick the rows
Set aside 20 rows to predict at the end. The models learn from a seeded sample of 8,000 of the rest, the most this table's width allows.
```
known = data.dropna(subset=[target])
new_rows = known.sample(20, random_state=1)
table = known.drop(new_rows.index).sample(8000, random_state=7)
```

Compare the models and predict

The models compete on the same folds against a simple guess, then the top-ranked one is fitted on the whole table. ROC AUC runs from 0.5, a coin toss, to 1, a perfect ranking, and a lower Brier score means the probabilities can be trusted more. The last table puts each prediction next to the real answer.

with Client() as client:
    evaluation = client.evaluate(table, target, task="classification", seed=7,
                                 models=["tabicl-v2", "kumo-medium"])
    model = evaluation.fit()

print(evaluation.summary()[["name", "rank", "roc_auc_mean", "brier_mean"]])
features = new_rows.drop(columns=target)
print(pd.DataFrame({"actual": new_rows[target].values, "predicted": model.predict(features),
                    "probability": model.predict_proba(features).max(axis=1)}))

What you get

Load from the meterEach new reading gets a load type and a probability.

Cheaper schedulingHeavy loads at peak price stand out at once.

Proof it worksThe leaderboard shows the score against a simple guess.

Try it

Use Open with to run the research datasets notebook in Colab, Kaggle or Jupyter and search for "steel industry energy consumption", or run the code above after pip install "welt-client[parquet]".

Open with

Read the docs

Type: Use case
Area: Energy
Question: What Load Is the Steel Plant Running?
Model: TabICLv2, Kumo Tabular Medium
Data: Steel Industry Energy Consumption, 35,040 rows
For: Energy managers and industrial engineers
Licence: CC BY 4.0