Capgemini-推动清洁能源转型-利用人工智能和第二代人工智能为可持续、更智能、更可靠的能源系统提供燃料（英）

Power the clean energy transitionFuel sustainable, smarter, and more reliable energy systems with AI and Gen AI.The energy sector has reached an inflection point. The transition to cleaner, greener energy sources is well underway, but the steady growth in the use of decentralized power systems such as solar, wind, and geothermal energy has meant that the grid is now more multi-faceted – and complex. And just as energy sources have become more disparate, energy demands have become less predictable thanks to trends such as extreme weather events, remote work, and changing consumer behavior. These dynamic demands and democratization of diverse energy sources have resulted in a palpable shift in the energy landscape – one that is bringing planning, production, and delivery systems to a tipping point as legacy energy management platforms struggle to operate effectively. Last year, renewable energy capacity grew by 50% globally, with China leading the way on solar power, and record increases in Europe, Brazil, and the US. But with this record-breaking growth, shifting economic and trading landscape comes challenges, as the industry straddles new and old systems, and works to serve the continued need for traditional, reliable, and affordable energy alongside this growing demand for renewables.2Power the clean energy transition I Fuel sustainable, smarter, and more reliable energy systems with AI & Gen AI.Traditional energy systems were built on static rules, siloed data, and centralized control structures that can’t respond quickly enough or scale effectively to meet today’s needs. They simply weren’t designed for the level of complexity seen today. And it’s not like the old fuel pipes are being turned off as the new ones are powered up – fossil-fuel-based energy systems are far from being retired. Energy companies must account for and accommodate the unique challenges that come with renewables, including their intermittent availability and fluctuating price points, while simultaneously navigating existing energy production and distribution systems.The pressing need for the energy sector is evident: to define a forward-looking data strategy that can address the challenges in managing this smart and dynamic ecosystem. This requires a strategy to govern the data from a new generation of smart meters and assets – time series and historical data, consumer data, maintenance and scheduling systems, substation, and grid data, not only at the source level but also on a modern data and AI platform.This strategy must consider how to maintain just the right amount of historical data needed for future AI use cases while complying with legal and regulatory requirements. And it must incorporate intelligent, data-driven systems that have the ability to leverage real-time data, machine learning, and AI to anticipate fluctuations in supply and demand, optimize the performance of distributed assets, automate trading of energy resources, and provide faster, more accurate