76 INTERVIEW directly into dashboards at the company’s French headquarters using AVEVA’s PI System. After verifying and contextualising the data, the team analyses performance and measures results against specific key performance indicators. This enables them to monitor 85 per cent of emissions accurately and use AI models to forecast the impact of operational changes. In one case, optimising power delivery at a single site cut carbon emissions by 15 per cent annually. Scaling this approach across TotalEnergies’ portfolio could result in tens of thousands of tonnes of carbon savings each year. Another example involves predictive and prognostic AI being implemented at a major US power producer as part of an overall reliability-centred maintenance programme. When a generating unit was brought back online after repairs, an operator inadvertently set the extraction steam temperature too low. Thankfully, predictive analytics caught this issue very early, so the company was able to quickly fix the problem, allowing it to prevent 2,000 tonnes per week of excess carbon dioxide going into the atmosphere and saving 100,000 gallons of water. What role does open-source technology play in amplifying AI-infused solutions? Open-source collaboration can accelerate the potential of AI and ML-enhanced energy networks. Open technologies establish a shared language for data exchange, ensuring compatibility across different systems. This enables data from sources like sensors, smart meters, weather stations and maintenance logs to be seamlessly integrated and analysed together. Historically, the proprietary nature of energy systems limited innovation. Today, open technologies allow data and ideas to be shared across departments, companies, industries and countries, driving deeper collaboration and faster progress in the energy sector. How will quantum computing impact the future of AI and energy efficiency? AVEVA recently signed a memorandum of understanding with Oxford Quantum Computing to explore how quantum power can reduce the energy load of AI. A Forbes study suggests that simple quantum apps can cut energy use by up to 50 per cent. Currently, it takes the equivalent energy of running 47 US homes for a year to fire up a supercomputer, while it takes the energy needed to boil just seven kettles of water to run a quantum computer. However, quantum has a number of hurdles to cross in order for it to become mainstream. It is prone to error, susceptible to noise, requires complex hardware and lacks scalability and reliability. A lot of work is being done to overcome these challenges. In the future, quantum is likely to provide a very energyefficient alternative to intensive computing, such as AI. Can you give an insight into how AI will transform AVEVA’s operations? Successful AI implementation will depend on building partnerships that drive large-scale innovation. A prime example is our collaboration with Microsoft, where we leverage AVEVA’s industrial expertise in the cloud to benefit customers worldwide. This approach is crucial to delivering the advantages of AI at scale. Additionally, we are actively working with governments and our industrial clients to continually refine AI models, enhancing efficiency and supporting the energy transition. Learn more about the advantages of implementing AVEVA’s industrial AI-based solutions at: bit.ly/3ZzcBhE AI can analyse sensor data from wind turbines to forecast asset failure and optimise performance Photo: Adobe Stock/zhengzaishanchu
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