Our client is a regional energy utility company responsible for supplying electricity from a combination of traditional power plants, photovoltaic systems, and wind farms. As renewable energy sources became a larger part of the energy mix, the utility faced growing volatility in production levels.
The utility’s operational challenges stemmed from the intermittent and weather-dependent nature of renewable energy. Legacy forecasting models were static and often failed to adapt to changing conditions or new infrastructure.
We built an energy production forecasting model that uses real-time and historical data to predict generation levels across multiple sources. The solution combined weather modeling and grid-level telemetry to generate highly localized, short-term forecasts.
Software Deployment
Infrastructure Audit and Source Identification
We began by auditing the client’s infrastructure, including PV arrays, wind farms, and thermal plants, to identify relevant data streams such as SCADA outputs, weather sensors, and energy production logs.
Data Cleaning and Predictive Signal Extraction
We built a robust preprocessing pipeline to normalize weather forecasts, fill in missing telemetry, and align sensor data with timestamped production records. Custom features were extracted to strengthen model inputs and improve prediction accuracy.
Forecasting Engine Design and Training
Our team designed a set of source-specific models and trained these models using historical data, backtested performance on past weather patterns, and tuned them for both intra-day and day-ahead accuracy.
Testbed Rollout and Operational Feedback
We deployed the system in a sandbox environment connected to a subset of the client’s energy assets. Forecasts were generated in parallel with existing models and manually validated to fine-tune thresholds, correct false positives, and test reliability before full rollout.
Live Rollout and Adaptive Monitoring
Following validation, the system was rolled out across the full generation portfolio. We implemented automated retraining pipelines, performance dashboards, and alerting systems to monitor model drift and prediction deviations.
The system didn’t rely on a one-size-fits-all approach. Instead, it ran specialized models, each tuned to the physics, infrastructure, and behavioral patterns of that source.
The forecasting engine continuously ingested updated weather data and grid telemetry to revise forecasts throughout the day.
Rather than operating as a standalone dashboard, the forecasts were embedded directly into the client’s energy management system.
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