Generative AI Techniques

This course is designed to provide a comprehensive understanding of generative AI techniques and their potential applications in the Electricity Generation and Renewable Energy Plants & Utilities industries. Participants will learn about various generative models, such as Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Transformers, and how they can be used to generate realistic synthetic data, enhance data augmentation, and solve domain-specific problems. The course covers the theoretical foundations of generative models, as well as practical implementation and evaluation strategies tailored to the Electricity Generation and Renewable Energy Plants & Utilities industries.

• Understand the fundamental concepts and theory behind generative AI models

• Implement and train GANs, VAEs, and Transformer-based generative models using deep learning frameworks

• Generate realistic synthetic data for various Electricity Generation and Renewable Energy Plants & Utilities use cases, such as power system simulations, renewable energy resource assessment, and load profiles

• Apply generative models for data augmentation to improve the performance of supervised learning tasks

• Utilize generative AI techniques for domain-specific applications, such as power system planning, renewable energy integration, and demand response optimization

1. Introduction to Generative AI

• Overview of generative models and their applications in the Electricity Generation and Renewable Energy Plants & Utilities industries

• Probability distributions and sampling techniques

• Generative vs. discriminative models

• Hands-on exercises: Implementing basic generative models (e.g., Gaussian Mixture Models, Hidden Markov Models)

2. Generative Adversarial Networks (GANs)

• GAN architecture and training process

• Variants of GANs (e.g., DCGAN, WGAN, CycleGAN)

• Evaluation metrics for GANs (e.g., Inception Score, Fréchet Inception Distance)

• Hands-on exercises: Training a GAN to generate synthetic load profiles or renewable energy resource data

3. Variational Autoencoders (VAEs)

• Variational inference and latent variable models

• VAE architecture and training process

• Conditional VAEs and their applications

• Hands-on exercises: Implementing a VAE for generating synthetic power system scenarios

4. Generative Transformer Models and Applications

• Transformer-based generative models (e.g., GPT, BERT)

• Fine-tuning generative Transformers for Electricity Generation and Renewable Energy Plants & Utilities text data

• Domain-specific applications of generative AI in the Electricity Generation and Renewable Energy Plants & Utilities industries (e.g., power system planning, renewable energy integration, demand response optimization)

• Hands-on exercises: Fine-tuning a generative Transformer model for generating synthetic power system reports or renewable energy project proposals

• Strong understanding of deep learning concepts and architectures (e.g., CNN, RNN, Transformers)

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

• Familiarity with probability theory and statistical concepts