Description

Artificial intelligence is increasingly embedded in energy systems that operate critical infrastructure, from grid optimization and load forecasting to wind turbine control and predictive maintenance. AI Safety for Energy Systems provides a practical governance and safety guide for deploying AI and machine learning in operational technology environments.

This book focuses on AI used in industrial control systems, where failures can result in physical damage, service disruption, or safety incidents. It explains how AI-driven decision-making differs from traditional automation and why conventional IT security controls are insufficient for operational contexts.

Readers are guided through safety cases, risk assessment techniques, and governance structures tailored to energy and utility environments. The book emphasizes integration with existing OT practices, reliability engineering, and regulatory expectations.

Key topics include:

AI and ML use cases in grid and energy operations Safety cases for AI-driven control and optimization Integrating AI with SCADA, DCS, and OT environments Risk management for model drift and automated decisions Governance and oversight for critical infrastructure AI

Written for energy and OT managers, this guide supports safe, resilient, and auditable AI deployment across power generation and grid operations.