ML Basics for Electricity Plants & Utilities Applications

This course is designed to provide a comprehensive foundation in the fundamental concepts and techniques of AI learning, specifically tailored for applications in the Electricity Generation and Renewable Energy Plants & Utilities industries. Participants will gain a deep understanding of the underlying principles and algorithms that power modern AI systems, and learn how to apply these concepts to solve real-world problems in electricity generation, renewable energy management, and utility operations.

• Understand the fundamental principles and techniques of AI learning and their relevance to the Electricity Generation and Renewable Energy Plants & Utilities industries

• Apply supervised, unsupervised, and reinforcement learning algorithms to solve problems in electricity generation, renewable energy management, and utility operations

• Develop a strong intuition for model selection, hyperparameter tuning, and performance evaluation

• Implement and deploy AI learning models using industry-standard tools and frameworks

• Communicate the results and insights obtained from AI learning models to both technical and non-technical stakeholders

1. Introduction to AI Learning

• Overview of AI learning and its applications in the Electricity Generation and Renewable Energy Plants & Utilities industries

• Types of learning: supervised, unsupervised, and reinforcement learning

• The AI learning process: data preparation, model selection, training, evaluation, and deployment

• Hands-on exercises: Setting up the development environment and working with electricity generation and renewable energy datasets

2. Supervised Learning

• Overview of supervised learning and its applications in electricity generation, renewable energy management, and utility operations

• Algorithms for classification and regression (e.g., logistic regression, decision trees, support vector machines)

• Model selection, hyperparameter tuning, and cross-validation techniques

• Hands-on exercises: Implementing supervised learning algorithms for load forecasting, fault detection, and renewable energy output prediction

3. Unsupervised Learning

• Overview of unsupervised learning and its applications in electricity generation, renewable energy management, and utility operations

• Algorithms for clustering, dimensionality reduction, and anomaly detection (e.g., k-means, PCA, autoencoders)

• Techniques for data visualization and interpretation

• Hands-on exercises: Applying unsupervised learning algorithms for customer segmentation, power quality monitoring, and renewable energy site selection

4. Reinforcement Learning

• Overview of reinforcement learning and its applications in electricity generation, renewable energy management, and utility operations

• Markov Decision Processes (MDPs) and the Bellman equation

• Algorithms for value-based and policy-based reinforcement learning (e.g., Q-learning, SARSA, policy gradients)

• Hands-on exercises: Implementing reinforcement learning algorithms for dynamic power dispatch, energy storage management, and demand response optimization

5. Advanced Topics and Applications

• Deep learning architectures for AI learning (e.g., convolutional neural networks, recurrent neural networks)

• Transfer learning and domain adaptation techniques for electricity generation and renewable energy data

• Real-world case studies and applications of AI learning in the Electricity Generation and Renewable Energy Plants & Utilities industries

• Hands-on exercises: Developing an end-to-end AI learning pipeline for an electricity generation or renewable energy problem

• Strong understanding of mathematics, including linear algebra, calculus, and probability theory

• Proficiency in programming with Python or R

• Familiarity with basic machine learning concepts and algorithms