How RWE & HPE Are Harnessing AI for Weather Forecasting

As the climate across the world poses unprecedented challenges to renewable energy operations, AI is emerging as a crucial tool for precise weather forecasting and resource management.

Hewlett Packard Enterprise (HPE)'s Private Cloud AI solution, developed in partnership with Nvidia, represents a breakthrough in this domain.

This platform combines sophisticated AI capabilities with private cloud infrastructure, enabling organisations to process massive weather datasets and generate rapid, accurate forecasts.

The technology's implementation by RWE, a leading renewable energy company, also marks a pivotal moment in the integration of AI into energy sector operations.

Through retrieval-augmented generation (RAG) and advanced data processing capabilities, HPE's solution promises to revolutionise weather modelling while allowing companies to maintain control over their data within their own infrastructure.

This development comes at a crucial time when the energy sector faces mounting pressure to optimise operations while advancing sustainability goals.

AI-driven weather modelling

RWE's Artificial Intelligence Research Laboratory, established in 2024 in Bellevue, Washington, will utilise HPE's AI-optimised private cloud to evaluate, fine-tune and infer weather models.

This approach leverages AI technology to enhance operational decision-making and strengthen RWE's position in the global renewable energy market.

Simon Tarplee, Head of Commercial AI at RWE Supply & Trading, emphasises: “As a leading provider of renewable energy, RWE is at the forefront of balancing the increasing power demands of the modern world with the societal challenge of protecting the climate.

“We see AI as a key enabler that allows us to optimise our processes and create new, sustainable solutions and set the ambitious goal to have a production training capable solution in-place by the end of 2024.”

The company has also set an ambitious goal to have a production-ready training solution in place by the end of 2024.

This initiative aligns with the broader trend in the industry, where AI tools like GraphCast, powered by Google and Pangu-Weather, developed by Huawei, have demonstrated the ability to generate forecasts in seconds with comparable reliability to classic methods.

Advancing energy transition

RWE's adoption of AI-driven weather modelling also aligns with its Growing Green strategy, which focuses on expanding its renewable energy portfolio.

The company is actively building new onshore and offshore wind farms, solar parks and battery storage facilities.

RWE has additionally committed to decarbonising its business in line with the 1.5-degree reduction pathway and aims to achieve net-zero emissions by 2040, emphasising the growing importance of AI in addressing climate challenges.

Fidelma Russo, Executive Vice President and General Manager of Hybrid Cloud and Chief Technology Officer at HPE, highlights the significance of AI-driven weather models in unlocking higher efficiencies for RWE's renewable energy portfolio: “The ability to fine tune and inference AI-driven weather models will be a key enabler to unlock higher efficiencies for RWE's renewable energy portfolio and will give them a significant competitive advantage in the market,” says Fidelma Russo, EVP & GM, Hybrid Cloud & CTO at HPE.

“HPE Private Cloud AI is a unique turnkey solution that enables RWE's researchers to reach the market quicker and handle massive datasets.”

Streamlined AI infrastructure

The HPE Private Cloud AI solution offers RWE a private cloud platform that can be deployed rapidly and handle large volumes of data required for weather models.

The system uses retrieval-augmented generation (RAG) to extract information efficiently.

This on-premise solution allows RWE's researchers to deploy new private cloud AI infrastructure within their own policies, facilitating faster experimentation and scaling of AI projects across various domains and regions.

The approach also aligns with the broader challenges identified in AI for weather forecasting, including data quality, model complexity, integration and validation.

By addressing these challenges, RWE aims to develop more accurate and timely predictions of extreme weather events, potentially saving lives and protecting critical national infrastructure.

The platform-based experience provided through the HPE GreenLake cloud will enable RWE's team to manage infrastructure, automate processes and orchestrate endpoints, workloads, and data, including sustainability metrics.

This comprehensive approach allows the research team to focus primarily on model development, potentially accelerating time to market and providing RWE with a competitive advantage in the energy sector.

"With HPE Private Cloud AI, we will have a turnkey solution that helps us to improve our forecast accuracy and optimise energy resource management globally," Simon concludes. 

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