Merge pull request #12 from XinFang88/Cybertraining-MSU

Cybertraining msu

pct/_toc.yml

@@ -13,6 +13,13 @@ chapters:
   - file: modules/01/env.md
   - file: modules/01/jupyter.md
   - file: modules/01/git.md
+- file: modules/02/intro.md
+  sections:
+  - file: modules/02/Time-Series-Analysis.ipynb
+  - file: modules/02/Solar-Power-Forecasting.ipynb 
+- file: modules/03/intro.md
+  sections:
+  - file: modules/03/power-system-optimization-problem.ipynb
 - file: modules/04/intro.md
   sections:
   - file: modules/04/linear-equations.ipynb

pct/modules/02/Intro.md

@@ -0,0 +1,33 @@
+
+# Data analytics and visualization in Python
+## Overview: Time Series Data Analysis 
+
+### Objective
+
+The goal is to perform an in-depth analysis of historic time series data of the power grid system of Great Britain over a span of multiple years, from 2016 to 2023. By leveraging tools like pandas, matplotlib, and seaborn, it aims to uncover trends, patterns, and insights across different time periods.
+
+Source of data: https://www.neso.energy/industry-information/balancing-services/frequency-response-services/historic-frequency-data
+
+### Key Components
+
+1.	**Data Loading**: Data is loaded from CSV files for each year and organized by month.
+2.	**Data Preprocessing**: The data is cleaned and combined for ease of analysis.
+3.	**Time Series Analysis**: Various metrics are computed, and monthly trends across different years along with yearly trends are compared.
+4.	**Data Visualization**: Graphs and visual representations are generated to communicate key insights.
+
+## Overview: Forecasting Solar-Energy Output
+### Objective
+The primary objective is to build and evaluate a predictive model, with a focus on understanding the relative importance of different features in making predictions. The model deals with structured data and aims to optimize prediction accuracy while providing explainability.  
+We use historical time-series data from a specified region in Mississippi from 2006 to analyze and forecast solar-energy output.
+
+### Key Components
+1. Data Preparation: Importing, cleaning, and preprocessing data for modeling.
+2. Model Development: Training machine learning models.
+   - ARIMA model
+   - Prophet model
+   - LightGBM model
+3. Feature Importance Analysis: Evaluating which features contribute the most to predictions.
+3. Visualization: Graphically representing feature importance for interpretability.
+
+
+