Data Analytics and Visualization#
Welcome to Module 02 of the PowerCyber Training program. This module provides essential data analysis and visualization skills for power systems engineers and researchers. Modern power systems generate vast amounts of data from SCADA systems, smart meters, market operations, and weather stations. The ability to efficiently process, analyze, and visualize this data is crucial for system operations, planning, and research.
Module Overview#
This module bridges the gap between the command-line tools you learned in Module 01 and the advanced numerical methods coming in Module 04. We begin with Python programming fundamentals, ensuring you have a solid foundation before moving to specialized libraries. Each lesson uses power system examples and real-world data to maintain relevance while building technical skills.
The module is structured to progressively build your capabilities. We start with core Python programming concepts, then introduce NumPy for efficient numerical computing, followed by Pandas for data manipulation. With these foundations, we explore data visualization techniques essential for communicating technical results. The module concludes with time series analysis methods widely used in load forecasting and renewable generation prediction.
Learning Objectives#
Upon completing this module, you will be able to:
Write Python programs to automate power system calculations and data processing tasks
Use NumPy arrays to perform vectorized operations on large-scale power system data
Manipulate and analyze power system datasets using Pandas DataFrames
Create publication-quality visualizations of power system data and results
Apply time series analysis techniques to forecast load and renewable generation
Prerequisites#
This module assumes you have completed Module 01 or have equivalent experience with:
Basic command-line operations in Linux/WSL
Creating and managing Python environments using conda/mamba
Running Jupyter notebooks
Basic Git operations for version control
No prior Python programming experience is assumed. We will build these skills from the ground up using power system contexts.
Module Structure#
The module consists of five comprehensive lessons:
Python Fundamentals for Power Systems: Core programming concepts including data types, control flow, functions, and error handling
NumPy for Numerical Computing: Array operations, linear algebra, and complex numbers for power flow calculations
Data Analysis with Pandas: Loading, cleaning, and analyzing real power system datasets
Data Visualization for Power Systems: Creating effective plots and visualizations for technical communication
Time Series Analysis and Forecasting: Analyzing temporal patterns and building forecasting models
Each lesson is designed to take approximately 2 hours to complete and includes extensive hands-on exercises with power system applications.
Approach#
This module emphasizes learning by doing. Every concept is introduced through power system examples, and you will work with real data from system operators and utilities. The exercises are designed to reinforce concepts while building practical skills you can immediately apply to your research or work.
We focus on open-source tools that are widely used in both academia and industry. The skills you develop here form the foundation for more advanced topics in optimization (Module 03), numerical methods (Module 04), and co-simulation (Module 06).
Getting Started#
Ensure your Python environment from Module 01 is activated and has the necessary packages installed. Each lesson will specify any additional requirements. Remember to save your work regularly and commit your progress to Git as you complete exercises.
Let’s begin with Python fundamentals tailored specifically for power systems applications.