EnergyPredictive AnalyticsPublic CloudAuroraMicrosoft Planetary Computer ProAzure Container RegistryAzure Container AppsAzure Application InsightsModel Context Protocol (MCP)Microsoft Foundry Models

Building the intelligence foundation for the energy transition

BKW FMB Energie AG · Switzerland

BKW, a Switzerland-headquartered energy and infrastructure company, needed a more scalable, integrated approach to working with complex weather and geospatial data to support decisions across energy production, grid management, and infrastructure services as renewable penetration and climate volatility rise. BKW partnered with Microsoft to combine Aurora (an AI weather foundation model from Microsoft Research AI for Science and Microsoft AI Weather), Microsoft Planetary Computer Pro with AI Weather Grid HD EU data catalogs, and Azure-native infrastructure (Azure Container Registry, Azure Container Apps, Azure Application Insights, and an MCP server) into a unified platform for AI-driven weather and geospatial intelligence, with BKW retaining governance over how insights are applied. The collaboration is described as an exploratory, foundation-building engagement rather than a production deployment, giving BKW's data scientists, domain experts, and technology teams a shared, governed platform and supporting its net-zero and sustainable energy goals.

Overview

BKW, a Switzerland-headquartered energy and infrastructure company, needed a more scalable, integrated approach to working with complex weather and geospatial data to support decisions across energy production, grid management, and infrastructure services as renewable penetration and climate volatility rise. BKW partnered with Microsoft to combine Aurora (an AI weather foundation model from Microsoft Research AI for Science and Microsoft AI Weather), Microsoft Planetary Computer Pro with AI Weather Grid HD EU data catalogs, and Azure-native infrastructure (Azure Container Registry, Azure Container Apps, Azure Application Insights, and an MCP server) into a unified platform for AI-driven weather and geospatial intelligence, with BKW retaining governance over how insights are applied. The collaboration is described as an exploratory, foundation-building engagement rather than a production deployment, giving BKW's data scientists, domain experts, and technology teams a shared, governed platform and supporting its net-zero and sustainable energy goals.

The challenge

BKW operates across energy production, grid management, and infrastructure services in a rapidly transforming energy landscape, and as renewable penetration rises and climate volatility increases, needed a more scalable, integrated approach to working with complex weather and geospatial data to support better decisions across disciplines. The challenge was not a shortage of data, but working with complex environmental, geospatial, and operational data at the scale and speed required to support better decisions.

The solution

BKW partnered with Microsoft to combine Aurora, the AI weather foundation model from Microsoft Research AI for Science and Microsoft AI Weather, with Microsoft Planetary Computer Pro (including the AI Weather Grid HD EU data catalogs) and Azure-native infrastructure — Azure Container Registry, Azure Container Apps, Azure Application Insights and other Azure components — into a unified platform for AI-driven weather and geospatial intelligence. A Model Context Protocol (MCP) server empowers BKW to unlock weather insights from agentic workflows with appropriate human review and oversight, while BKW retains governance, domain expertise and control over how insights are applied.

Predictive AnalyticsAgentic AI

Reported business value

The collaboration is in its exploratory, foundation-building phase rather than a production deployment. BKW now has a shared, governed data and AI platform that lets data scientists, domain experts and technology teams access AI-ready geospatial and atmospheric data and iterate faster between disciplines, moving from fragmented, ad hoc experimentation toward a more coherent, reusable approach to AI-enabled analysis, and building organizational understanding toward responsible AI deployment at scale.

Related entries

Other energy entries in the register.

All entries
EnergyNatural Language ProcessingPublic Cloud

Building a clean energy future with natural language analytics

Williams, a large-scale natural gas infrastructure operator, deployed Databricks AI/BI Genie to give commercial, regulatory, accounting and technical staff natural-language, self-serve access to analytics. The team flattened 27 disparate tables into SQL models that Genie Spaces reason over, encoding internal acronyms and business logic into Genie's instructions, powered by Databricks Unity Catalog. A data request that previously took an analyst five days now completes in seconds with validated accuracy, and the weekly backlog of data requests dropped from up to ten to one or two, freeing analysts for predictive modeling and enterprise projects.

96/100HighWilliamsPrimary source
EnergyPredictive AnalyticsPublic Cloud

Plenitude builds machine learning models on Databricks to forecast energy demand and renewable production

Eni-owned energy company Plenitude, which serves 10 million households and businesses across Europe, uses statistical models and machine learning on the Databricks Data + AI Platform to forecast customer energy consumption at hourly and daily granularity, forecast wind and solar generation from its renewable asset portfolio, and run customer segmentation and propensity models across 60 implemented use cases.

100/100HighPlenitudePrimary source
EnergyPredictive AnalyticsUnknown

TotalEnergies builds Pangea 5 supercomputer with Dell Technologies and NVIDIA

TotalEnergies is building Pangea 5, a next-generation supercomputer developed with Dell Technologies and NVIDIA that will increase the company's computing power to support seismic imaging, advanced simulation and AI-driven research in the energy sector.

92/100HighTotalEnergiesPrimary source
EnergyNatural Language ProcessingPublic Cloud

AES accelerates renewable energy adoption with Claude on Google Cloud

AES, a global energy company, built a multi-agent AI system using Claude on Google Cloud to automate internal safety audits of its renewable energy assets. The system uses document processing agents, task breakdown agents, and report generation agents to process hundreds of pages of safety documentation, evaluate compliance, and produce audit reports. Audit reports that previously took up to two weeks to complete (across roughly 1,550 internal audits annually) can now be generated in about an hour, a 99% reduction in time, alongside a 10-20% improvement in audit accuracy and a 99% reduction in audit costs.

96/100HighAESPrimary source

Was this helpful?

Your feedback helps us improve our use case database