Digital intelligence and automation in industrial and energy systems examines how intelligent technologies are changing manufacturing, robotics, mobility, and power equipment. It also covers renewable energy systems. The book brings together diverse views on industrial automation, machine learning, digital twins, and other topics such as 3D perception, smart microgrids, and photovoltaic manufacturing. It does not treat automation as just a technical tool. Instead, it treats it as an integrated approach that includes sensing, modeling, and prediction alongside control and optimization. Readers can find discussions about neural scene representations in industrial environments and hybrid forecasting for renewable energy systems. The book also covers app-based mobility as an automation platform and AI and robotics in PV module production. Robust neurodynamic control for robotic manipulators is emerging alongside physics-based modeling in transformer manufacturing. The advantage is its cross-domain perspective. It connects digital intelligence with practical applications in industry and energy. This shows how data, models, and algorithms can improve system performance and resilience. It also improves quality, scalability, and sustainability. It suits researchers, engineers, and graduate students. Educators and practitioners interested in the future of intelligent automation could find it helpful.