Energy Forecasting - Electricity demand, prices, and generation mix

An energy analytics organisation sought to enhance its forecasting capabilities for electricity demand, wholesale prices, and generation mix. The existing process relied heavily on manual analysis, which limited the speed and scalability of producing high quality forecasts across multiple countries. The goal was to create a repeatable, automated system that could provide accurate results under different policy and market scenarios.

I led the development of a suite of Python based time series and machine learning models, drawing on a wide range of inputs including historical demand data, weather patterns, fuel prices, and policy changes. These models were designed to handle both short term operational forecasts and longer term scenario analysis, allowing for flexible planning and decision making.

The solution was deployed with an automated data pipeline, ensuring that inputs were continuously updated and forecasts generated without manual intervention. Model performance was monitored through accuracy metrics and back-testing, with regular refinement to account for market volatility and seasonal trends.

The improved forecasting capability provided stakeholders with timely, data driven insights, supporting energy policy discussions, investment strategies, and grid management decisions.