Digital Energy and Optimization
Start Date | End Date | Venue | Fees (US $) | ||
---|---|---|---|---|---|
Digital Energy and Optimization | 05 Oct 2025 | 09 Oct 2025 | Dubai, UAE | $ 3,900 | Register |
Digital Energy and Optimization | 21 Dec 2025 | 25 Dec 2025 | Jeddah, KSA | $ 4,500 | Register |

Digital Energy and Optimization
Start Date | End Date | Venue | Fees (US $) | |
---|---|---|---|---|
Digital Energy and Optimization | 05 Oct 2025 | 09 Oct 2025 | Dubai, UAE | $ 3,900 |
Digital Energy and Optimization | 21 Dec 2025 | 25 Dec 2025 | Jeddah, KSA | $ 4,500 |
Introduction
The world runs on energy, or better yet, wastes a lot of energy, as the energy is wasted during its production, transport, and consumption. Incredible as it may seem but the world could save almost 20% of energy if it would just improve its consumption, by delivering energy only when and where it is required. This computing was not available before, but as the era of Big Data came about the advancement in the Data Mining and Artificial Intelligence (AI) should provide the answer to the questions of energy-saving and optimization of energy resources use.
By using AI and digitization methods creating the virtual twins of the energy and industry systems help the entities and communities achieve a high level of energy savings as well as high optimization of industrial processes, as they provide the possibilities of experimentation, innovation, simulation, and forecasting.
This training course will feature:
- Data Mining techniques and principles
- Artificial Intelligence algorithms and models
- Neural networks
- Simulation and creation of digital twins
- Use of Data Mining and Artificial Intelligence for Energy Preservation and Optimization
Objectives
- Apply the data mining methodology for energy usage patterns
- Effectively utilize Artificial Intelligence algorithms for real-time optimization
- Identify key areas where the Data Mining and Artificial Intelligence can be utilized
- Understand the benefits through the example cases
- Use Data Mining and Artificial Intelligence methods for optimization of spinning reserves
By the end of the training, participants will be able to:
Training Methodology
The program is delivered in a combination of lecture-style and computer-based training. In addition, a significant amount of time is set aside for small working group activity when addressing case study problems. Extensive use is made of case study material to underline the key aspects of the course and to give the delegates exposure to current best practice.
Who Should Attend?
This training course is suitable for a wide range of professionals. These include:
- Professionals who want to learn techniques of Data Mining and Artificial Intelligence
- Team Leaders, Supervisors, Section Heads, and Managers
- Professionals who have an interest in Data Science
- Technical Professionals including those in Maintenance, Engineering & Production
- Project Managers
- Anyone interested in optimization and energy consumption reduction
Course Outline
DAY 1: Data Mining and Pattern Recognition
- Data Mining Process
- Data Preparation
- Association and Pattern Recognition
- Data Mining in the Energy Industry
- Data Mining-clusters and Outliers
DAY 2: Artificial Intelligence Algorithms
- Artificial Intelligence Development
- Linear Regression
- Logistic Regression
- Decision Tree
- Support Vector Machine
- Other Algorithms Applied in Artificial Intelligence (AI)
DAY 3: Energy Distribution Planning and Optimization
- Energy Storage Planning
- Managing Incidents and Instrument Failures
- Energy Grid Management
- Energy Consumption Forecasting
DAY 4: Developing Digital Twins
- Digitization of Industry and Energy
- Optimal Power Flow Problem Formulation
- Neural Network Application to Optimal Power Flow
- Particle Swarm Optimization for Optimal Power Flow
- Total Transfer Capability Enhancement by Evolutionary Algorithm
DAY 5: Simulation, Machine Learning, and Smart Contracts
- Dynamic Simulation of Industry Systems
- Simulation of Unit Commitment Problem
- Machine Learning for Renewable Energy
- Forecasting Renewable Energy Generation
- Smart Contracts within the Energy Industry