Embedding

This course is designed to provide a deep understanding of embeddings, a powerful unsupervised learning technique, with a focus on its applications in the Electricity Generation and Renewable Energy Plants & Utilities industries. Participants will learn how to represent complex data structures, such as time series, graphs, and text, in a lower-dimensional space while preserving their semantic relationships. The course covers various embedding techniques and their applications in power system analysis, renewable energy forecasting, and utility customer behavior modeling.

• Understand the concept of embeddings and their applications in the Electricity Generation and Renewable Energy Plants & Utilities industries

• Implement and evaluate various embedding techniques for time series, graphs, and text data

• Apply embeddings to solve power system analysis tasks, such as load forecasting and fault detection

• Utilize graph embeddings for power grid topology analysis and renewable energy site selection

• Leverage text embeddings for utility customer sentiment analysis and feedback classification

1. Introduction to Embeddings

• Overview of embeddings and their role in unsupervised learning

• Applications of embeddings in the Electricity Generation and Renewable Energy Plants & Utilities industries

• Vector space models and their properties

• Hands-on exercises: Implementing basic embedding techniques (e.g., one-hot encoding, bag-of-words) for electricity generation and renewable energy data

2. Time Series Embeddings

• Time series data in the Electricity Generation and Renewable Energy Plants & Utilities industries (e.g., load profiles, renewable energy output)

• Time series embedding techniques (e.g., Dynamic Time Warping, Time Series Kernels)

• Deep learning approaches for time series embeddings (e.g., Recurrent Autoencoders, Temporal Convolutional Networks)

• Applications of time series embeddings in the Electricity Generation and Renewable Energy Plants & Utilities industries (e.g., load forecasting, renewable energy output prediction)

• Hands-on exercises: Developing a time series embedding model for electricity generation or renewable energy time series data

3. Graph Embeddings

• Introduction to graph theory and power grid topology analysis

• Random walk-based embeddings (DeepWalk, node2vec) for power grid fault detection and localization

• Matrix factorization-based embeddings (Laplacian Eigenmaps, Graph Factorization) for renewable energy site selection and power flow optimization

• Applications of graph embeddings in the Electricity Generation and Renewable Energy Plants & Utilities industries (e.g., grid resilience analysis, distributed energy resource management)

• Hands-on exercises: Implementing graph embedding techniques on power grid and renewable energy network data

4. Text Embeddings and Applications

• Text data in the Electricity Generation and Renewable Energy Plants & Utilities industries (e.g., utility customer feedback, maintenance reports)

• Word embeddings (Word2Vec, GloVe, FastText) for utility customer sentiment analysis

• Document embeddings (Doc2Vec, TF-IDF) for maintenance report classification and clustering

• Contextualized embeddings (ELMo, BERT) for named entity recognition and relation extraction in electricity generation and renewable energy text data

• Hands-on exercises: Applying text embedding techniques to solve a real-world Electricity Generation or Renewable Energy Plants & Utilities problem

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

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

• Familiarity with unsupervised learning concepts and dimensionality reduction techniques