COURSE OVERVIEW:

The meaning of intelligent automation and control systems for energy efficiency refers to the application of advanced sensors, logic controllers, and software algorithms to autonomously manage energy-consuming assets in real-time. This course focuses on the evolution from "static" setpoint control to "dynamic" optimization, where Artificial Intelligence (AI) and Machine Learning (ML) anticipate demand shifts and environmental changes. By implementing intelligent automation, organizations can eliminate the human-error factor in energy management, ensuring that every kWh is utilized only when and where it is truly needed.

The scope of this training encompasses the hierarchy of industrial and building automation—from Field Devices and PLCs (Programmable Logic Controllers) to SCADA (Supervisory Control and Data Acquisition) and cloud-based Energy Management Systems (EMS). It examines the technicalities of "Closed-Loop" control and the implementation of advanced strategies like "Proportional-Integral-Derivative" (PID) tuning and "Model Predictive Control" (MPC). The course provides a deep dive into the integration of Internet of Things (IoT) protocols such as MQTT, OPC-UA, and BACnet, which enable seamless data flow across diverse energy assets.

Coverage includes the analysis of "Smart Lighting" (DALI), "Occupancy-Based HVAC," and "Automated Demand Response" (ADR) as primary tools for demand-side efficiency. The course addresses the critical challenge of "Data Integrity" and "Cybersecurity" in connected energy systems. Participants will gain practical expertise in designing automated energy optimization routines, utilizing "Edge Computing" for low-latency decision-making, and formulating strategies to transition from legacy "Manual" operations to "Autonomous" energy-efficient facilities.

COURSE OBJECTIVES:

After completion of this course, the participants will be able to:

1.  Identify the core components of an intelligent automation architecture.

2.  Differentiate between PLC, SCADA, and DCS in energy management contexts.

3.  Optimize energy setpoints through "PID Tuning" and "Fuzzy Logic" control.

4.  Implement "Model Predictive Control" (MPC) for complex thermal systems.

5.  Integrate IoT sensors using MQTT, Zigbee, and LoRaWAN protocols.

6.  Design "Occupancy-Driven" automation for lighting and HVAC.

7.  Utilize "Edge Computing" for real-time energy data processing.

8.  Implement "Automated Demand Response" (ADR) to lower peak charges.

9.  Analyze the role of "Machine Learning" in predictive energy forecasting.

10.  Ensure "Data Cybersecurity" for connected industrial and building assets.

11.  Develop a "Digital Twin" for simulating energy optimization routines.

12.  Formulate a technical roadmap for "Full Autonomous Energy Management."

TARGET AUDIENCE:

This course is designed for Automation Engineers, Control Systems Specialists, Energy Managers, Facility Directors, IT/OT Integration Leads, and Sustainability Analysts focused on smart building and industrial technology.

TRAINING COURSE METHODOLOGY:

A highly interactive combination of lectures, discussion sessions, and case studies will be employed to maximize the transfer of information, knowledge, and experience. The course will be intensive, practical, and highly interactive. The sessions will start by raising the most relevant questions and motivating everybody to find the right answers. The attendants will also be encouraged to raise more of their questions and to share in developing the right answers using their analysis and experience. There will also be some indoor experiential activities to enhance the learning experience. Course material will be provided in PowerPoint, with necessary animations, learning videos, and general discussions.

The course participants shall be evaluated before, during, and at the end of the course.

COURSE CERTIFICATE:

National Consultant Centre for Training LLC (NCC) will issue an Attendance Certificate to all participants completing a minimum of 80% of the total attendance time requirement.