Semi-Supervised learning & Transfer Learning

This course is designed to provide an in-depth understanding of semi-supervised and transfer learning techniques and their applications in the Electricity Generation and Renewable Energy Plants & Utilities industries. Participants will learn how to leverage unlabeled data and pre-trained models to improve the performance and efficiency of machine learning solutions for various tasks relevant to power systems, renewable energy forecasting, and grid optimization.

• Understand the principles and motivations behind semi-supervised and transfer learning

• Apply semi-supervised learning algorithms, such as self-training, co-training, and graph-based methods, to leverage unlabeled power system and renewable energy data

• Implement and fine-tune pre-trained models for transfer learning in the electricity generation and renewable energy domains

• Develop domain adaptation techniques to bridge the gap between source and target domains in power systems and grid applications

• Deploy semi-supervised and transfer learning solutions for renewable energy forecasting, power grid anomaly detection, and grid optimization

1. Introduction to Semi-Supervised Learning

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

• Assumptions and scenarios for semi-supervised learning

• Self-training, co-training, and multi-view learning algorithms

• Graph-based semi-supervised learning methods (e.g., label propagation, manifold regularization)

• Hands-on exercises: Implementing semi-supervised learning algorithms on power system and renewable energy datasets

2. Transfer Learning and Pre-trained Models

• Introduction to transfer learning and its benefits for the electricity generation and renewable energy domains

• Types of transfer learning: feature extraction, fine-tuning, and domain adaptation

• Pre-trained models for time series forecasting (e.g., LSTM, Transformer) and grid image analysis (e.g., ResNet, DenseNet)

• Fine-tuning strategies and best practices for transfer learning in power systems and renewable energy applications

• Hands-on exercises: Fine-tuning pre-trained models for renewable energy forecasting and power grid image classification

3. Domain Adaptation Techniques

• Problem formulation and challenges in domain adaptation for power systems and grid data

• Discrepancy-based methods (e.g., Maximum Mean Discrepancy, Correlation Alignment)

• Adversarial-based methods (e.g., Domain Adversarial Neural Networks, Adversarial Discriminative Domain Adaptation)

• Reconstruction-based methods (e.g., Domain Separation Networks, Cycle-Consistent Adversarial Networks)

• Hands-on exercises: Implementing domain adaptation techniques for power grid anomaly detection and cross-domain renewable energy prediction

4. Applications and Case Studies

• Renewable energy forecasting using semi-supervised learning and transfer learning

• Power grid anomaly detection and fault diagnosis with pre-trained models and fine-tuning

• Grid optimization and control using domain adaptation techniques

• Case studies of semi-supervised and transfer learning in the Electricity Generation and Renewable Energy Plants & Utilities industries

• Hands-on exercises: Developing a semi-supervised or transfer learning solution for a specific electricity generation or renewable energy use case

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

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with supervised learning concepts and techniques (e.g., classification, regression, neural networks)

• Knowledge of unsupervised learning methods (e.g., clustering, dimensionality reduction) is beneficial but not required