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Energy

Load forecasting, asset optimization, and environmental compliance

Forecast energy demand, optimize asset performance, and meet environmental reporting requirements — with automated model retraining as seasonal patterns shift.

The Challenges

Load Variability

Weather, seasonal patterns, and economic activity create complex demand curves that shift year over year.

Asset Degradation

Turbines, transformers, and solar panels degrade over time — early detection prevents costly failures and outages.

Environmental Reporting

Carbon emissions tracking and renewable portfolio standards require accurate, auditable data processing and reporting.

Grid Integration

Integrating distributed energy resources (solar, wind, batteries) requires forecasting intermittent generation sources.

How CorePlexML Helps

AutoML

Load Forecasting Models

AutoML trains time-series models on weather, calendar, and historical consumption data to predict demand at hourly resolution.

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MLOps

Asset Health Monitoring

MLOps continuously monitors deployed models against incoming sensor data with automated drift detection and alerts.

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ML Studio

Generation Scenario Testing

ML Studio lets planners model renewable intermittency scenarios and see predicted grid stability impact.

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Privacy Suite

Compliance Data Pipelines

Privacy Suite and Dataset Builder handle environmental data anonymization, standardization, and audit trail generation.

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SDK Example

example.py
from coreplexml import CorePlexMLClient

client = CorePlexMLClient(
    base_url="https://api.coreplexml.io",
    api_key="sk_your_api_key"
)

# Upload historical load + weather data
dataset = client.datasets.upload(
    project_id="proj_grid",
    file_path="load_weather_2025.csv",
    name="Regional Load Data"
)

# Train load forecasting model
experiment = client.experiments.create(
    project_id="proj_grid",
    dataset_version_id=dataset["dataset_version_id"],
    target_column="load_mw",
    max_models=20,
    max_runtime_secs=900
)

# Deploy with scheduled retraining (weekly)
deployment = client.deployments.create(
    project_id="proj_grid",
    model_id=experiment["leader_model_id"],
    strategy="direct"
)

Expected Impact

2.8%
Forecast MAPE
38%
Asset Failure Prevention
96%
Grid Balancing Accuracy
90%
Reporting Automation

Ready to get started?

Try CorePlexML free — no credit card required. Train your first model in under 10 minutes.

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