In November 2022, the UK joined the Net Zero Government Initiative as a partner and signatory, committing to achieving net zero emissions by 2050.
Since then, the Committee on Climate Change (CCC) has raised concerns about the UK’s ability to deliver on its promise, while the current Labour government scaled back its £28bn green investment plan earlier this year while still in opposition. As a result, there has rightly been doubt as to whether achieving net zero emissions is a realistic possibility within the timescales set.
To ensure that net zero emissions remains an achievable goal rather than becoming another pipe dream, energy companies should explore how they can harness emerging technologies to maximise efficiency. One example is recovery augmented generation, known as RAG.
What is RAG?
In essence, RAG is a tool that combines the retrieval of relevant information with the generation of useful responses. Imagine a super-intelligent assistant that can sift through vast amounts of data, pinpoint relevant data points, and provide recommendations or create reports based on that data. That’s exactly what RAG does, acting as a behind-the-scenes AI hero.
RAG in the energy sector
The uncertainty and unpredictability of the energy sector make it difficult to deploy it widely. However, RAG enables energy companies to make better use of the data at their disposal, painting a clearer picture of likely outcomes and allowing them to shift from a reactive to a proactive approach.
Here are some examples of areas where RAG can be used to improve performance.
Predictive maintenance
The energy sector is typically capital-intensive, and effective management of assets can mean the difference between success and failure. It can be difficult to predict when a piece of machinery or equipment might fail, but RAG can analyze historical data and recommend maintenance before costly failures occur. This leads to fewer disruptions and greater confidence in the stability of the energy supply.
Improved Wind Farm Efficiency
Wind farm operations can be optimized with the use of RAG. The AI that drives this technology can analyze satellite imagery, weather patterns, and historical turbine performance data to suggest the best wind turbine placements and maintenance schedules. This can lead to significant improvements in total production, as well as efficiency gains and reductions in unscheduled maintenance costs.
Automated Compliance
The energy industry is highly regulated, with policies that change frequently. RAG can navigate the latest regulations, compliance laws, and guidelines, ensuring that companies avoid fines and penalties while maintaining safe and legal operations.
Predict Solar Power Generation
RAG can generate customized energy saving strategies by examining large amounts of consumption data, helping companies reduce waste, save costs, and move to a more sustainable operation. Similar technology can be applied to forecast solar power generation capacity and match it with historical customer demand data, integrating weather forecasts and real-time solar irradiance data.
RAG Leads the Charge
Innovations like RAG have reached a level of maturity where they are ready for large-scale deployment. RAG can sift through a myriad of reports, historical market data and forecasting models to help companies understand the future of energy prices. This information can be used to make smarter buying and selling decisions, potentially saving millions in the market.
Energy companies that adopt this technology, having recognised its ability to produce immediate returns, will hopefully then expand their horizons further, looking for other innovations that can make their operations more intelligent and intuitive. In this way, AI algorithms could play a central role in energy companies’ operations, taking full advantage of the technology’s ability to forecast, create predictions and suggest actionable insights.
RAG in Large Language Models
RAG can also play a critical role in integrating Large Language Models (LLMs) into various business areas for energy players. We’ve all seen the hype around LLMs like OpenAI’s ChatGPT, which generally work well. But each company has its own characteristics and environment, with its own documents, procedures and specifications.
Therefore, it is extremely difficult to effectively apply LLMs in companies, but RAG can help. It provides this missing layer of company-specific context to LLMs, which in turn means it provides relevant business value that energy companies can leverage. In essence, you can try to dig a hole with a spoon, but a shovel will do it better, which means RAG can be a transformative tool in implementing LMMs.
Achieving net zero with the help of technology
Net zero ambitions depend on a wide range of factors: there is no single element that will be the key to success. However, the way energy companies approach new technologies and their willingness to experiment with the latest innovations will certainly be a factor in building a cleaner world. RAG promises to play a significant role in this transition.
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This article is originally published on global.techradar.com
