Industry Dilemma & Traditional Limits
Renewable intermittent output (wind, solar) combined with complex weather shocks makes traditional statistical models prone to catastrophic forecast errors during peak hours.
Case · Energy & Power
Energy & Power Systems
Multi-scale renewable forecasting, power grid dispatch, and dynamic market risk.
Target Specs (Solution Concept)
- Forecast Horizon
- Varies by model
- Sampling Granularity
- 1-sec to daily
- Uncertainty Quantiles
- P10 / P50 / P90
Solution Concept (Planned)
Concept: the general time-series model returns quantile envelopes (e.g. P10–P90) for metered telemetry and other energy series, cleaned first on the SequenxAI Platform, as an input to dispatch and storage strategy; weather-forecast covariates are planned.
Live
Beta · Technical demoLive forecasts: Energy
Forecasts issued automatically from public data streams in this sector and scored point by point once the actuals arrive; forecasts are never changed after they are issued. This is a summary of each source's latest forecast; the full charts, point-by-point scores and history are on the live board.
Technical demo, not advice for any decision; see the Terms of Service.
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Typical Scenarios
- Regional solar & wind generation forecasting
- Microgrid and energy storage dynamic charge/discharge arbitrage
- Substation transformer overload warning and transmission line flow-limit and congestion prediction
Case deep-dive · Solution concept (planned)
Grid Load Dispatch & Renewable Power Forecasting
Turn regional load and utility-scale wind/solar output into quantile intervals at 15-minute (day-ahead 96-step) and similar granularities, as an input to spot bidding and grid assessments; the forecast span depends on the chosen model’s maximum steps per request and the data frequency, and numerical weather predictions as covariates are planned.
Context
Utility-scale wind, solar and battery storage integration, combined with extreme weather, puts sustained pressure on grid dispatch and on renewable plant assessments. Generators are assessed on dispatch deviation under the 'Two Rules', while dispatchers need P10–P90 intervals across day-ahead 96-step and 16-step rolling horizons to plan spinning reserve, which a single point forecast cannot express.
Decision meaning
Control centers and energy trading desks can use quantile intervals as an input to reserve allocation and spot settlement; model output provides objective inference, not automated AGC supervisory control commands.
What the sequences look like
- Primary: regional aggregate load, wind farm/solar plant active power (15-minute regular 96 points)
- Covariates (planned; current models do not use them): numerical weather predictions (NWP: irradiance, wind speed, ambient temperature), grid operational regimes, and calendar effects
- Dirty-data patterns: RTU telemetry clock drifts, SCADA communication dropouts, and AGC inverter curtailment artifacts
How to use it on the SequenxAI Platform
- Organize multi-station long tables with unique_id / ds / y; known-future NWP covariates can be sent and are reported per column, model use is planned
- Cleaning handles dropouts, duplicates and off-grid timestamps and returns structured data_quality audits; telling curtailment from faults needs business rules and is not a current cleaning capability
- Read P10 / P50 / P90 quantile envelopes as an input to day-ahead 96-step spot bidding curves and battery dispatch buffers
Boundaries & non-claims
- No unverified accuracy ranking claims (e.g. refusing unsubstantiated '< 3% RMSE' blanket promises)
- For power secondary-system scenarios, on-premises deployment with forward and reverse isolation devices can be agreed per project to meet security-protection requirements
- Human-in-the-loop: dispatchers and plant supervisors retain absolute override and control responsibility
Enterprise Co-Creation
Evaluate forecastson your own time series
Have unique sequence and decision challenges? Book a benchmark or private PoC to confirm feasibility and boundaries on your own data.