Unsupervised Learning Dimensionality 

This course is designed to provide a comprehensive understanding of unsupervised learning techniques, with a focus on dimensionality analysis and clustering methods, specifically tailored for applications in the Electricity Generation and Renewable Energy Plants & Utilities industries. Participants will learn how to extract meaningful insights from unlabeled data, identify hidden patterns, and develop effective strategies for data preprocessing and feature engineering in electricity generation, renewable energy, and utility operations contexts.

• Understand the fundamental principles of unsupervised learning and its applications in the Electricity Generation and Renewable Energy Plants & Utilities industries

• Apply dimensionality reduction techniques to improve model performance and data visualization

• Implement and evaluate various clustering algorithms for customer segmentation, power quality monitoring, and renewable energy site selection

• Develop effective strategies for data preprocessing and feature engineering in unsupervised learning tasks

• Leverage unsupervised learning techniques to solve real-world problems in the Electricity Generation and Renewable Energy Plants & Utilities domains

1. Introduction to Unsupervised Learning

• Overview of unsupervised learning and its differences from supervised learning

• Types of unsupervised learning tasks and their applications in the Electricity Generation and Renewable Energy Plants & Utilities industries

• Challenges and considerations in unsupervised learning for electricity generation, renewable energy, and utility operations data

• Hands-on exercises: Exploring and visualizing unlabeled electricity generation and renewable energy datasets

2. Dimensionality Analysis

• The curse of dimensionality and its implications for machine learning in electricity generation, renewable energy, and utility operations

• Principal Component Analysis (PCA) for linear dimensionality reduction

• t-SNE and UMAP for non-linear dimensionality reduction and data visualization

• Autoencoders and their applications in dimensionality reduction and anomaly detection for electricity generation and renewable energy data

• Hands-on exercises: Applying dimensionality reduction techniques to electricity generation and renewable energy datasets

3. Clustering Methods

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

• K-means clustering and its variations (e.g., K-medoids, Mini-batch K-means)

• Hierarchical clustering (Agglomerative and Divisive) for customer segmentation and renewable energy site selection

• Density-based clustering (DBSCAN) for anomaly detection and outlier analysis in power quality monitoring data

• Evaluation metrics for clustering performance (e.g., Silhouette score, Calinski-Harabasz index)

• Hands-on exercises: Implementing and evaluating clustering algorithms on electricity generation and renewable energy case studies

4. Advanced Topics and Applications

• Gaussian Mixture Models (GMM) for probabilistic clustering of electricity generation and renewable energy data

• Self-Organizing Maps (SOM) for data visualization and clustering in power grid analysis

• Combining unsupervised and supervised learning techniques (e.g., clustering for feature engineering in load forecasting)

• Real-world applications of unsupervised learning in the Electricity Generation and Renewable Energy Plants & Utilities industries (e.g., demand response optimization, distributed energy resource management)

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

• Solid understanding of mathematics, including linear algebra and statistics

• Proficiency in programming with Python or R

• Familiarity with basic machine learning concepts and supervised learning algorithms