Context
Seasonal weather and climate variability can materially affect energy planning and operations, particularly where precipitation and temperature influence production and system conditions.
Challenge
Uncertainty in medium‑ to long‑term climate conditions (e.g., precipitation and temperature) affects energy planning, hydro management and trading. Traditional forecasting provides limited probabilistic insight at seasonal horizons, increasing operational risk and constraining decisions.
AI Solution
The solution uses machine learning to identify patterns and trends in global climate indices and their correlation with key energy variables such as precipitation and production. It produces seasonal probability forecasts (below / average / above normal) to support planning, hydro reservoir management, and energy trading decisions.
Impact & evidence
Intended measurement areas include forecast accuracy and error metrics at seasonal horizon, adoption rate by target users, and financial impact through improved planning and reduced unplanned costs. The solution is also positioned as enabling direct integration with business decision processes through advanced probabilistic modelling and integration of multiple global climate indices.
Implementation / Adoption
Replicability requirements include access to global climate indices, historical meteorological and hydrological data, internal energy production data, and cloud-based analytics platforms, with operational adoption requiring alignment with planning and energy assessment processes.
The implementation timeline involves model design, data preparation, and feature selection using historical climate indices, followed by deployment through operationalization on Azure (cloud platform) and integration with Power BI (business intelligence platform), and realization through use by climatologists and energy assessment teams in planning and trading activities.
Data
Provider-generated or external data includes global climate indices; meteorological and precipitation data; historical meteorological and hydrological data. Customer-provided or internal data includes internal energy production data; historical energy production and hydro data; internal energy production and asset data. Enabling components include cloud infrastructure (for example, Azure), data science and climatology expertise, and integration with operational and reporting systems.
Future enhancements
Future improvements include model enhancements through back-testing, sharper probabilistic outputs, longer forecast horizons, and expansion to additional use cases such as wind forecasting in North America and Brazil and temperature forecasting in Europe.
