Case study 04 · Energy & utilities
Predicting demand before the grid feels it.
A demand-forecasting engine combining weather, calendar, and historical load data for grid operators in India and Germany.
- Sector
- Energy / Utilities
- Geography
- India · Germany
- Operators
- Government + private
- Integration
- Existing dispatch tools
01 / The challenge
Forecasting for a changing grid.
Grid operators needed demand forecasts that could respond to changing load patterns. Their existing forecasts were too coarse and slow to update for the dispatch decisions they supported.
The challenge was to account for the different costs of overestimating and underestimating demand, while fitting into the systems operators already used.
02 / The approach
Train for the decisions that matter.
The forecasting engine used a Penalty Optimizer to weight errors by their operational cost. A forecast miss during peak demand could carry a different penalty from the same miss during a quieter period.
Weather, calendar, and historical load data supplied the model inputs. A retraining loop incorporated new regional load data, and the engine was designed to integrate with existing dispatch tools.
03 / The outcome
A potential reduction in electricity loss.
The project covered government grid operators in India and private utilities in Germany. The reported opportunity was a potential 12% reduction in electricity loss through better alignment of generation and demand.
Forecasts were integrated with existing dispatch workflows, giving operators an additional input for planning generation.
What we built
The system, in parts.
- 01
Penalty-weighted forecasting
Weights forecast errors according to their operational cost.
- 02
Multiple data signals
Combines weather, calendar, and historical load data in the forecast.
- 03
Regional retraining
Updates regional models as new load data becomes available.
Technology used
- Time-series ML
- Penalty Optimizer
- Python
- Weather + load fusion
Selected work delivered across GOT AI, Rocket Farm Studios, and Full Cortex in engineering, technical leadership, and advisory roles. Results are as reported by the project. TensorFlow, the TensorFlow logo and any related marks are trademarks of Google Inc. Logos identify technology the project used and link to their makers; no endorsement is implied.
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