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.
Ce que vous allez maîtriser
- 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