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Load ForecastingVisioAccurate, granular load forecasting with audit-grade confidence.
EcoMetricx deploys a full-stack, AWS-native load forecasting pipeline into your infrastructure — turning high-granularity AMI meter data into reliable short, medium, and long-term gross and net load forecasts, with hierarchical reconciliation and LLM-assisted quality assurance.
92 Signal confidenceAWS-nativeSingle-tenant
Outcomes
Why Utilities Adopt This
ΣForecast from meter-level AMI demand insights
↗Forecast load with hierarchical reconciliation
↻Automate QA, reporting, and approvals
▣Retain complete audit lineage in S3
Forecasting Scope
Granular forecasts at every level, over every horizon
Reliable, robust load forecasts that are accurate and realistic — reflecting actual market and customer behavior in your service area, while minimizing the impact of outliers and anomalies.
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Meter-Level Demand Insights
High-granularity AMI data characterizes consumption behavior by customer class and end-use, improving load modeling accuracy from the ground up.
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Every Aggregation Level
Forecasts at the customer segment, feeder, substation, weather-zone, and system level — one consistent hierarchy from meter to system peak.
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Every Horizon
Short, medium, and long-term forecasts of both gross and net load, for user-defined historical and forward-looking time horizons.
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Net-Load Ready
Distributed energy resources — rooftop solar, EV charging, and battery storage — represented explicitly, so net-load forecasts track an evolving grid.
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Weather-Aware
Weather and other influencing factors built into every forecast, with external weather data ingested and updated on a configurable schedule.
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Outlier-Resilient
Outliers and anomalies are detected and their influence minimized, so forecasts reflect real customer behavior rather than data noise.
Features
Twelve capabilities, one pipeline
Gross + net load forecasting
Short, medium, and long-term forecasts of gross and net load across every aggregation level.
Chronos 2 + XGBoost fallback
Zero-shot global baseline forecasts with robust fallback when Chronos is unavailable.
Hierarchical reconciliation
Bottom-up forecasts are reconciled to system totals with proportional scaling and optional MinT extensions.
Probabilistic scenarios
Monte Carlo sampling produces quantiles and risk-aware planning distributions.
LLM QA + policy gate
LLM summaries are validated by deterministic thresholds to prevent unsafe actions.
Automated monitoring
MAPE/WAPE drift signals trigger retraining and approval workflows.
Audit-grade artifacts
Every run produces complete lineage in S3 for compliance and audit review.
Event-driven orchestration
Step Functions, EventBridge, and Lambda coordinate the pipeline at scale.
Secure by default
Runs inside your AWS account with least-privilege IAM policies.
Human approval gates
Critical findings are escalated to SNS for manual oversight.
Domain-tuned reporting
LLM narrative reports translate metrics into executive-ready insights.
Deployment-ready code
Idempotent CLI scripts stand up the entire stack in minutes.
Planning Model
A bottom-up planning and operational model
The system is represented through the aggregation of individual component behaviors — transparent, configurable, and built for scenario planning.
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Granular Resource Representation
Individual load components and DERs — rooftop solar, EV charging, and battery energy storage — modeled with defined capacity assumptions, duty cycles, and operational constraints.
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High-Resolution Temporal Modeling
Hourly and sub-hourly intervals accurately capture daily load shapes, peak demand, and operational variability.
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Configurable Bottom-Up Inputs
Transparent, adjustable assumptions for load growth and resource availability at the asset or sector level — see how individual component changes affect the overall system.
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Multi-Scenario & Sensitivity Analysis
Develop and evaluate multiple planning scenarios, and run sensitivities on key variables such as EV adoption levels or solar resource variability.
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Analytical Output & Documentation
Comprehensive results in tabular and graphical formats — exportable and suitable for planning analyses, regulatory filings, and stakeholder reporting.
Result
A planning model you can interrogate: change an assumption, rerun a scenario, and see the system-level impact — with the reasoning documented.
Workflow
A four-stage pipeline architecture
1 · Ingest + QA
Validate curated panel coverage and data integrity before compute.
2 · Calibrate + Train
Run model training and load calibration in parallel for speed.
3 · Forecast + Reconcile
Generate forecasts and scenarios, and reconcile hierarchies to system totals.
4 · Report + Monitor
Deliver narratives, metrics, and policy-governed decisions.
Operations & Enterprise Fit
Built for daily operations, wholesale markets, and your IT standards
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Day-Ahead Ready
Timely forecast runs meet day-ahead operational timelines and data exchange requirements, supporting wholesale market analysis and operational forecasting.
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Continuous Calibration
Ongoing calibration of load data and configurable update frequencies keep forecast accuracy on target as conditions change.
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Accuracy You Can Measure
A documented methodology for validating and measuring forecast accuracy, with automated drift monitoring and retraining workflows.
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Dashboards & Reporting
Visualize, compare, and analyze multiple forecast models, assumptions, and results — with reporting across aggregation levels and exports in industry-standard formats.
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Enterprise Integration
Single sign-on with multi-factor authentication, secure file transfer, and API access for modeling and reporting — including integration of internal and system-operator forecasts.
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Supported End to End
Training for administrators and end users, help desk support with defined service levels, and upgrades that keep pace with market rules and regulatory changes.
Security
Security & Governance
Single-tenant deployment
The pipeline deploys within your own AWS account.
Privacy-by-design
Data protection built into the architecture from the start.
Least-privilege IAM
Access governed by least-privilege IAM policies.
Encrypted storage
Encrypted S3 storage with optional KMS.
Audit lineage
Complete audit lineage retained for every run.
Your data stays yours
All data stays in your AWS account, in buckets you control.
Engagement
A four-phase engagement model
Phase 1
Discovery and data contract
Validate data availability, forecast definitions, and planning constraints.
Phase 2
Infrastructure deployment
Provision AWS resources, networking, and IAM policy boundaries.
Phase 3
Calibration and validation
Tune thresholds, hierarchies, and scenarios with your domain experts.
Phase 4
Operational handoff
Enable monitoring, reporting, and a shared operations playbook.
FAQ
Frequently Asked Questions
Is this an open-source project?+
No. EcoMetricx deploys and operates the pipeline as a managed engagement in your infrastructure.
Where does our data live?+
All data stays in your AWS account, in buckets you control.
Can we use our own models?+
Yes. The pipeline is modular and can accommodate custom forecasting components.
How long does deployment take?+
Initial deployment can be completed in days, with calibration and validation following.
Forecast load with audit-grade confidence
Deploy a full-stack, AWS-native load forecasting pipeline into your infrastructure.