COURSE OVERVIEW:

The meaning of AI and Machine Learning for predictive energy analytics refers to the application of advanced algorithms to vast datasets to forecast energy demand, optimize consumption patterns, and predict asset failures. This course bridges the gap between traditional energy management and modern data science, demonstrating how neural networks and regression models can identify efficiency opportunities that are invisible to the human eye. By shifting from reactive monitoring to proactive intelligence, organizations can significantly reduce operational costs and enhance the reliability of their energy infrastructure.

The scope of this training covers the entire data pipeline, from sensor integration and data ingestion to model deployment and visualization. It explores various machine learning architectures, including supervised learning for load forecasting, unsupervised learning for anomaly detection, and reinforcement learning for autonomous system optimization. The course examines how AI can be applied across different sectors, including smart buildings, industrial microgrids, and renewable energy integration, providing a comprehensive toolkit for the modern energy analyst.

Coverage includes the technical challenges of data cleaning, feature engineering, and model validation specifically within the context of time-series energy data. The course addresses the ethical and security considerations of AI in critical infrastructure, ensuring that automated decisions are transparent and robust. Participants will gain hands-on insights into how AI-driven predictive maintenance can extend the life of energy assets like transformers and turbines, while simultaneously optimizing the dispatch of distributed energy resources to maximize economic and environmental returns.

COURSE OBJECTIVES:

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

1. Identify high-value use cases for AI and Machine Learning within the energy value chain.
2. Implement data pre-processing techniques to handle missing or noisy meter and sensor data.
3. Develop predictive models for short-term and long-term energy load forecasting.
4. Utilize anomaly detection algorithms to identify energy theft or equipment malfunctions.
5. Apply clustering techniques to segment energy consumers based on usage patterns.
6. Design reinforcement learning frameworks for autonomous HVAC and lighting control.
7. Integrate weather and occupancy data into energy models for enhanced accuracy.
8. Evaluate the performance of different ML models using metrics like MAPE and RMSE.
9. Deploy predictive maintenance models to forecast the Remaining Useful Life (RUL) of assets.
10. Implement "Explainable AI" (XAI) to ensure energy decisions are interpretable by stakeholders.
11. Navigate the cybersecurity requirements for AI-driven energy management systems.
12. Formulate a business strategy for scaling AI initiatives across an energy portfolio.

TARGET AUDIENCE:

This course is designed for Energy Managers, Data Scientists, Systems Integrators, Facilities Engineers, Sustainability Officers, and IT Professionals who are looking to leverage advanced analytics to optimize energy performance and asset reliability.

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.