Forecast solar and wind energy production using AI and ML models. PySquad builds accurate, real-time forecasting systems for energy companies and renewable operators.
Increased revenue through better trading decisions
Adaptive systems that improve over time
Results depend on scope, integrations and adoption.
The operating context
A clearer view of the whole operation.
Solar and wind generation changes with weather, site conditions, equipment behavior, and operating constraints. When production forecasts rely on coarse weather inputs or static models, dispatch, grid coordination, and trading teams receive estimates that may not reflect current plant conditions.
Where friction builds
Why renewable energy forecasts become unreliable
Renewable operators often have historical generation, weather, SCADA, and equipment data available, but these sources are not always combined effectively. Forecast models built on limited inputs can lose reliability when cloud cover, wind conditions, seasonal patterns, or asset behavior changes.
Forecasting also needs ongoing evaluation. Accuracy can vary by site, horizon, and weather regime, so operators need predicted-versus-actual tracking, uncertainty visibility, and controlled model recalibration instead of treating one trained model as permanently accurate.
Current approach
Use historical averages for forecasts
Keep weather and plant data separate
Rely on static forecasting models
Review forecast errors manually
Operational impact
Forecasts miss changing plant conditions
Weather shifts reduce forecast reliability
Planning decisions use stale estimates
Model performance degrades unnoticed
Inside the solution
The capabilities behind the operation.
Review the functional scope, then discuss the requirements specific to your team.
AI Energy Forecasting Models
Use machine learning models to forecast solar and wind generation from historical production, weather, and operational data.
Weather and Environmental Data Integration
Combine irradiance, wind speed, temperature, cloud, and other relevant weather inputs with plant data.
Real Time Plant Data Pipelines
Ingest production signals from SCADA systems, IoT sensors, APIs, and other operational sources.
Multi Horizon Forecasting
Generate forecasts across short term, intraday, day ahead, and longer planning horizons based on operational requirements.
Forecast Performance Dashboards
Track predicted versus actual generation, forecast error, trends, confidence ranges, and model performance.
Model Monitoring and Retraining
Monitor forecast accuracy and retrain models with new production and weather data as operating conditions change.
Collect and analyze historical, real-time, and weather data
02
Design and train machine learning forecasting models
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Integrate with operational systems and dashboards
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Continuously optimize models with new data
Our approach
We build forecasting systems around the actual operating conditions of each renewable energy asset. Historical generation, SCADA and IoT signals, weather inputs, and other relevant variables are combined into machine learning pipelines that are tested against real production data. Models can then be monitored and retrained as new data becomes available.
How it connects
The complete workflow, in one view.
Make an informed decision
Is this the right fit?
The right solution starts with the right operating requirements.
What data is required for renewable energy forecasting?
Typical inputs include historical generation data, weather data, SCADA signals, sensor readings, and relevant plant operating information. The exact data requirements depend on the asset type, site conditions, forecast horizon, and business use case.
Do you build custom forecasting models for each solar or wind site?
Yes. Forecasting models can be designed around the specific asset, geographic location, equipment characteristics, historical generation patterns, and available weather and operational data.
How do you measure energy forecasting accuracy?
We compare predicted generation against actual production using appropriate forecasting error metrics. Models are evaluated across different weather conditions and time horizons so teams can understand where forecast performance is strong and where further optimization is needed.
Can forecasts integrate with grid, dispatch, or trading systems?
Yes. Forecast outputs can be exposed through APIs, dashboards, scheduled exports, or other integration methods so they can support grid planning, dispatch operations, energy trading, and internal decision workflows.
Can the forecasting models update as new data becomes available?
Yes. We can implement monitoring and periodic retraining workflows that incorporate new production and weather data. This helps the forecasting system adapt as seasonal patterns, equipment behavior, and operating conditions change.
Start with your requirements
Let’s define your next step.
Tell us what needs to work better, the systems you use, and the scope you have in mind.
