AI in energy
AI in energy: demand and generation forecasting, grid balancing and optimization, predictive maintenance of assets, and trading.
Energy and utilities companies sit on vast physical and sensor infrastructure, from generation assets to grids to meters, making them a prime setting for applied AI. This block covers how core AI concepts map onto load forecasting, predictive maintenance, grid optimization and trading, where the technology genuinely moves the needle across the value chain versus where it is hype, and how to evaluate vendors and build realistic ROI cases. It closes with the governance layer specific to critical infrastructure: regulatory expectations, model risk in safety-critical and market-facing systems, and the pre-deployment checks that prevent costly or dangerous failures. The goal is sector-fluent judgment, not generic AI literacy.
What you'll master
- Explain core AI techniques (forecasting, computer vision, optimization, digital twins) using energy-sector examples and terminology
- Identify high-value AI use cases across generation, grid operations, trading and customer segments, and distinguish proven applications from overhyped ones
- Assess an AI vendor or pilot proposal using sector-appropriate evaluation criteria and build a realistic ROI and adoption case
- Apply a governance checklist covering regulatory compliance, model risk and safety controls before approving an AI deployment on critical infrastructure
Key terms
Modules
Covers core AI applications in energy: demand forecasting, grid optimization, predictive maintenance, and trading.
Covers how to identify use cases, assess data readiness, vet vendors, and quantify ROI when scaling AI.
Covers the governance, risks, bias, and safety checks utilities must apply to energy AI.
Latest articles
Recent articles from the blog that apply to Energy & Utilities.
- Read how Duke Energy cut turbine failures using sensor AIDuke Energy built one of the most operationally consequential AI deployments in U.S. generation by wiring sensor data into machine learning models that flag failures weeks before they happen. The mechanics of what they did, and what transfers to your assets, are worth examining closely.
- When the AI is confident and the grid goes darkAn AI system's confident wrong answer is dangerous in any industry. In power grid operations, where a single bad dispatch decision can cascade into a NERC reliability violation and a multi-million-dollar blackout, the stakes are categorically different from a chatbot giving a customer a bad product recommendation.