Renewable energy production is inherently variable, influenced by weather, equipment health, and environmental factors. Accurate forecasting is essential for grid planning, power trading, operational efficiency, and revenue optimisation. Traditional statistical models often fail to capture complex patterns in solar irradiance, wind speed, temperature, and plant behaviour.
PySquad develops AI-driven forecasting systems using machine learning, deep learning, and domain-specific models tailored for solar and wind assets. Our solutions enable operators to predict output with high accuracy, optimise dispatch planning, reduce curtailment, and improve financial performance.
Problem Businesses Face
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High variability in power output due to weather conditions.
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Limited visibility into short-term and long-term generation trends.
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Inefficiencies in grid coordination and power trading decisions.
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Manual forecasting methods leading to inaccurate predictions.
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Difficulty integrating weather data into operational systems.
Our Solution
PySquad builds AI-powered forecasting tools that combine historical plant data, real-time sensor data, and high-resolution weather inputs. Our ML pipelines generate short-term, day-ahead, and long-term forecasts for solar and wind farms.
Our solution includes:
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ML models for energy output prediction.
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Integration with SCADA, IoT meters, and weather APIs.
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Predictive insights for grid dispatch and trading decisions.
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Automated alerts for expected dips or surges.
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Custom dashboards for operators and plant managers.
Key Features
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High-accuracy forecasting using ML and deep learning.
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Weather-based prediction models (irradiance, wind speed, temperature).
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Real-time data ingestion from IoT and SCADA.
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Dashboard with trend lines, confidence intervals, and KPIs.
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Forecasting for 15-min, hourly, daily, and long-term intervals.
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API integration for grid operators and trading platforms.
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Auto-retraining models for continuous accuracy improvement.
Benefits
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Improved operational planning and grid coordination.
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Increased revenue through accurate power trading decisions.
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Reduced curtailment and better load balancing.
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Enhanced reliability for plant forecasting and reporting.
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Future-ready AI systems that improve over time.
Why Choose PySquad
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Strong expertise in ML, energy analytics, and IoT integrations.
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Experience working on forecasting models for renewable operators.
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Human-first dashboards accessible for both engineers and managers.
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Scalable cloud-based pipelines with automated retraining.
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End-to-end delivery: data engineering, ML modelling, deployment, and support.
Call to Action
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Want accurate solar and wind energy forecasting?
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Need AI-driven insights for better trading and dispatch planning?
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Looking to integrate weather and IoT data into your workflow?
Work with PySquad to build high-accuracy, scalable energy forecasting models.