Why AI for Energy?
Organizations in the energy sector face numerous significant challenges, such as energy demand volatility, the imperative of reducing carbon emissions, and the need to improve operational resilience and efficiency with aging infrastructure.
Innovations in AI machine learning, deep learning, and generative AI (GenAI) are empowering organizations to overcome these hurdles by leveraging insights from advanced analytics, demand forecasting, distribution optimization, and many more use cases.
Benefits of AI in Energy
AI can help energy sector organizations unlock new efficiencies across energy resourcing, generation, distribution, asset maintenance, and customer journeys. The capability of AI to analyze vast amounts of data means that AI technologies can identify patterns more consistently and more accurately than humans, leading to more-accurate predictions and more-insightful recommendations.
For example, a McKinsey report on AI use by energy companies found that AI-powered scheduling helped prevent unnecessary truck rolls and improve worker productivity in the field by up to 30 percent.1 In another example, a power plant operator used AI-driven recommendations to boost heat rate optimization—the power plant’s ability to efficiently convert fuel into electricity—by up to five percent.1
AI Use Cases in Energy
There are many energy sector use cases where AI can help improve operations, with new use cases still being developed. Here are just a few examples:
- Improving efficiency and safety for asset maintenance: AI computer vision‒equipped drones can help inspect and maintain assets even as they continue to run, enabling inspections without having to shut down operations and enhancing safety for workers in the field.
- Deeper insights from data: AI analytics can streamline logistics workflows for material orders, predictive maintenance schedules for extending the lifespan of assets, and truck roll scheduling to help improve field productivity.
- Enhanced efficiency for power grids: AI-enabled smart power grids and smart substations go beyond traditional supervisory control and data acquisition (SCADA) systems to enable remote management of grid systems. This gives utility companies greater control to automate energy distribution patterns, maintain equipment, and enhance resiliency against power outages.
- Renewable energy integration and carbon emissions reduction: AI workflows can assist in managing and integrating renewable energy sources into power grids by forecasting renewable outputs and analyzing source selection against consumption patterns, helping companies contribute to their environmental sustainability goals.
- AI in Cybersecurity: As power grids become attractive targets for cybercriminals, AI threat detection can help automate attack detection, prevention, and response strategies. AI can also help to enhance authentication methods for the multitude of devices and systems that make up energy distribution infrastructure.
Generative AI (GenAI) Use Cases in Energy
GenAI powered by large language models (LLMs) has captured popular interest in recent years and offers opportunities for groundbreaking use cases in the energy sector, going beyond traditional machine learning and deep learning approaches:
- More-satisfying customer experience journeys: GenAI can help enable more empathetic customer experiences with chatbots that engage customers with greater intelligence and more-human-like responses. Retrieval-augmented generation (RAG) also makes it possible for AI chatbots and personal assistants to reference enterprise-specific knowledge bases to output more-relevant answers. GenAI systems could also potentially be trained on highly technical manuals to help extend the capabilities of technicians in the field with AI personal assistants.
- Smart power grid simulation: Energy companies can potentially use gen AI to help simulate and generate optimal grid configurations, test various demand scenarios and outage response strategies, and plan the integration of new energy sources.
- Deeper insights from unstructured data: For advanced analytics use cases in support of predictive maintenance for assets, equipment, and infrastructure such as power lines, energy companies can leverage GenAI to ingest and analyze data from a wider variety of sources, including audio/video, imagery, weather forecasts, and more.
The Future of AI in Energy
The future of AI in the energy sector promises not only to enhance current systems and processes but also to transform the way energy is produced and consumed. AI can bring more automation to power plants and grids by assisting in critical decision-making in energy distribution, fault correction, and maintenance scheduling with minimal human intervention. AI-driven robotics will likely become more sophisticated and involved in inspections, maintenance, and repairs with the ability to mitigate risk to human workers in hazardous environments.
AI may also become instrumental in optimizing the chemical processes for carbon capture, utilization, and storage (CCUS) technologies and help monitor system performance and predict maintenance needs. These potential use cases highlight AI’s capability to deeply integrate into every aspect of the energy sector, driving efficiency, sustainability, and resilience.