Hydroelectric Generation Forecast

A forecasting and simulation model helps anticipate hydroelectric generation, understand key drivers and improve energy trading decisions
Hydroelectric Generation Forecast
Forecast Accuracy
One-month error reduced to 2.31 GWh
4 GWh Improvement
Forecast error improved by 4 GWh
Scenario Analysis
Model variables used to simulate outcomes
Energy Trading Impact
€750K impact on energy purchase and sale
A forecasting model for smarter hydroelectric decisions
A forecasting model for smarter hydroelectric decisions
Hydroelectric forecasting: clearer drivers, scenarios and decisions
Objectives
Improve generation forecasting and simulation
Opportunity
Use internal, external and weather-related data to support decisions
Forecasting with limited visibility

Prediction models lacked automation and diagnostic clarity

Key Challenges
Imprecise forecasts
Imprecise forecasts
Existing models were not accurate enough
Low automation
Low automation
Forecasting processes were poorly automated
Driver uncertainty
Driver uncertainty
Key generation drivers were not fully understood
Deviation diagnosis
Deviation diagnosis
Teams could not identify why deviations occurred
Solution

Transforming field operations through digital BDR model

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Structured BDR actions

Defined tasks, priorities, and tracking

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Performance measurement

KPIs across adoption, volume, execution

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Digital tools enablement

Apps, image recognition, task validation

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Structured enablement and integrated support ecosystem

Training, onboarding, and multi-level support -powered by chatbots and workflows- to drive adoption and scalable operations

Impact
Scalable digital growth

Improved adoption, execution, and customer experience across global operations

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