Our Approach

Applying real-time, continuously improving models to continuously evolving grid challenges.

The Challenge

Increased grid variability

Modern energy systems are more dynamic and less predictable than ever. Predict+ addresses:

Demand volatility: Rapid electrification, behavioral shifts, and DER growth make historical baselines unreliable.

Generation intermittency: Solar and wind variability introduce operational and market uncertainty.

Market disruption: Geopolitical instability, price spikes, and structural changes strain traditional forecasting models.

Operational risk: Forecast errors lead to imbalance penalties, inefficient dispatch, and compliance exposure.

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Inputs
Engineering
Models
Decisions
Feedback
Forecast Intelligence System

Unified AI platform for power demand, market price, and renewable generation forecasting

UtilityRetail Energy ISOIPPTrader
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Data Inputs
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Weather & Satellite Feeds
  • NWP Models: HRRR, ECMWF, NAM, ICON
  • Satellite / cloud motion vectors
  • Temperature, wind, irradiance
  • Extreme ramp alerts
Predict+ ingests multi-source NWP forecasts and satellite imagery, dynamically weighting each source based on historical skill for your specific location and time horizon.
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ISO / Market Data
  • ISO data: PJM, ERCOT, CAISO, NYISO
  • LMPs, congestion, outages
  • Fuel & grid signals
Real-time and day-ahead market data streams are continuously processed, including locational marginal prices, congestion indicators, and fuel cost signals across all major ISO regions.
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Meters / SCADA / AMI
  • Smart meter, feeder, substation
  • EMS, historian data
  • Customer mix, BTM data
Granular telemetry from smart meters, SCADA systems, and AMI infrastructure provides the real-time operational data foundation for high-accuracy load and generation forecasting.
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Renewable Asset Data
  • Site metadata & layout
  • Historical solar & wind
  • Maintenance, curtailment
Asset-level performance history, site configuration, and maintenance records enable precise per-asset and fleet-level generation forecasting with degradation awareness.
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Feature & Data Engineering Layer
Data cleaning · lag-based features · net/load logic · Digital twin portfolio modeling
Predict+ Forecast Orchestrator
Load Models
ISO, utility, customer load forecasting
Advanced time-series ML models trained on historical consumption patterns, weather correlations, and calendar effects.
Price Models
DA & RT LMP & price forecasting
Ensemble models combining market fundamentals, congestion analysis, and real-time supply/demand signals.
Renewable Models
Solar + wind / Fleet forecasting
Physics-informed ML models leveraging Tigo MLPE telemetry and multi-NWP ensemble weather data for probabilistic forecasts.
Uncertainty Engine
Confidence scores & error bands
Quantifies forecast uncertainty through probabilistic distributions, providing confidence intervals and risk envelopes.
Decision & Analytics Layer
🔔 Alerts & Qualification
  • Ramp / Peak alerts
  • Price spike warnings
  • Renewable scoring
📈 Portfolio Analytics
  • Bid / Hedging support
  • Procurement optimization
  • Forecast analysis
🖥 API / Dashboard / EMS
  • Forecast dashboard
  • API / SFTP / EMS outputs
  • Trading platform integration
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Feedback & Learning Loop
Actuals vs Forecasts
Backtesting KPIs
Continuous Improvement
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5-min to monthly forecast horizons
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Nodal to portfolio scale
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Load + Price + Renewables
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Probabilistic + Explainable AI
PREDICT+ BY TIGO ENERGY — FORECAST INTELLIGENCE SYSTEM

Data & methodology

Predict+ is built on a multi-source data foundation and adaptive AI methodology designed to maintain accuracy through the conditions that break other forecasting platforms.

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Data Sources (Weather, Market, Asset-Level, Historical)

Predict+ leverages a robust, multi-source data ecosystem:

  • Real-time and forecasted weather inputs
  • Historical consumption and generation profiles
  • Market pricing and settlement data
  • Asset-level telemetry and DER data
  • Calendar effects and macroeconomic indicators

This combination of granular and macro-level inputs strengthens forecast reliability.

Modeling Approach (ML/AI, Ensembles, Continuous Learning)

Predict+ employs:

  • Advanced machine learning models optimized for time-series forecasting
  • Ensemble approaches to reduce bias and improve stability
  • Continuous learning pipelines that retrain as new data arrives
  • Performance validation and model monitoring

The platform is engineered to maintain performance under both stable and volatile conditions.

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Forecast Horizons and Update Frequency

Predict+ supports multiple time horizons:

  • Intraday (sub-hourly updates)
  • Day-ahead
  • Week-ahead
  • Month-ahead
  • Long-term planning scenarios

Update frequency is configurable to match operational and trading requirements, enabling rapid adjustments when market or grid conditions change.

Common forecast types

Designed for asset-level precision and portfolio-scale visibility.

  • Intermittent renewable generation: solar, wind
  • Thermal, hydroelectric generation
  • System, substation-level demand modeling
  • Peak demand, load forecasting
  • Electrification and new asset additions
  • Portfolio balancing

Provides a holistic view of asset, system, and portfolio level performance.

Risk assessments

Designed for asset-level precision and portfolio-scale visibility.

  • Market volatility
  • Risk modeling
  • Market exposure modeling

Provides a holistic view of asset, system, and portfolio level performance.

Try it out

The Predict+ approach has been tested and refined through some of the most drastic geopolitical changes in recent history. See the results with your own data in a customized deployment.

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