Context
Accurate short and medium‑term weather forecasting enables better energy market decisions and supports lower‑impact operations and maintenance planning.
Challenge
Power generation operations are highly exposed to weather variability, creating uncertainty in production forecasting, asset dispatch, and maintenance planning. Traditional forecasting approaches lack the temporal granularity and integration required to support short‑ and medium‑term operational decisions, leading to sub‑optimal energy market positioning, increased imbalance risk, and avoidable O&M costs.
AI Solution
MeteoFlow enables optimal energy market decisions and low-impact O&M planning through integrated short and medium -term weather forecasting.
MeteoFlow integrates state‑of‑the‑art weather forecasting techniques, leveraging machine learning, artificial intelligence, and big‑data technologies. The solution has evolved over 15 years to incorporate advanced AI approaches and support integrated forecasting for operational and commercial decision‑making.
Impact & evidence
The solution reduces forecast error for production, directly lowers imbalance costs in energy markets and improves reliability of operational and commercial decisions. The solution was recognized as one of the Top 100 AI projects worldwide in the 2025 Global AI & SDG Index.
Implementation / Adoption
“Implementation requires access to reliable historical and real‑time meteorological data, asset‑level data from renewable installations, and sufficient data infrastructure with an appropriate computational environment to support big‑data processing and machine‑learning deployment, supported by strong governance and data quality controls.
Deployment followed an MVP approach covering one region and one technology over one year, with subsequent scaling to additional regions achieved within an additional 2-3 months.”
Data
Inputs include international and in-house forecasting meteorological models and internal operational data; customer or operational inputs include historical and real-time meteorological data from reliable sources and asset-level data from renewable installations (location, technology, capacity, operational constraints, and real-time data), with data availability requiring cleansing and supported by sufficient data infrastructure and a computational environment for machine learning.
Future enhancements
Future enhancements involve improving forecast accuracy by applying new AI algorithms and including new sources of input data.
