Part 1:Data analytics and visualization in Python

Part 1:Data analytics and visualization in Python#

Overview: Time Series Data Analysis#

Objective#

This module aims to provide an in-depth analysis of historical time series data from the power grid system of Great Britain spanning multiple years (2016–2023). The objective is to analyze grid frequency trends, detect anomalies, and understand seasonal variations using Python-based data analytics and visualization tools. By leveraging libraries such as pandas, matplotlib, and seaborn, this module enables users to extract meaningful insights from large datasets.

Source of data:Historic Frequency Data - National Grid ESO, https://www.neso.energy/industry-information/balancing-services/frequency-response-services/historic-frequency-data

Key Learning Components#

Data Acquisition and Loading#

Downloading and managing multiple CSV files corresponding to different years. Using pandas to read and consolidate data efficiently. Handling large datasets by structuring them into manageable monthly and yearly formats. Data Preprocessing

Cleaning and standardizing data for consistency.#

Handling missing values, outliers, and erroneous entries. Merging and restructuring datasets to facilitate comprehensive time series analysis. Exploratory Time Series Analysis

Computing key statistical metrics (mean, variance, standard deviation of frequency values).#

Identifying seasonal patterns and long-term trends. Comparing frequency deviations across months and years. Detecting anomalies or sudden frequency fluctuations using statistical methods. Data Visualization and Interpretation

Part 2: Overview: Forecasting Solar-Energy Output#

Objective#

This module focuses on developing and evaluating predictive models for solar energy output using historical time series data. The primary goal is to understand the factors influencing solar power generation and enhance forecasting accuracy. Additionally, the module emphasizes model explainability by analyzing feature importance.

Data Source: Historical solar energy generation data from a specified region in Mississippi, covering years from 2006 onward.

Key Learning Components#

Data Preparation#

Importing, cleaning, and formatting raw solar energy generation data. Handling missing timestamps and irregular time intervals. Feature engineering: incorporating weather data (temperature, cloud cover, humidity) to improve model accuracy.

Time Series Forecasting Models#

Implementing and comparing multiple forecasting models:#

ARIMA (AutoRegressive Integrated Moving Average) – Suitable for linear time series forecasting. Facebook Prophet – Handles seasonality and external factors effectively. LightGBM – A gradient-boosting model for high-performance forecasting.

Evaluating model performance using RMSE, MAE, and R² scores.#

Feature Importance Analysis#

Identifying key factors influencing solar power output. Using SHAP (SHapley Additive exPlanations) values to interpret feature contributions. Exploring how cloud cover, temperature, and seasonal changes impact predictions.

Visualization and Interpretability#

Plotting actual vs. predicted solar energy generation. Visualizing feature importance using bar charts, heatmaps, and SHAP summary plots. Comparing model performance and explaining prediction uncertainties.

Expected Outcomes#

By the end of this module, participants will be able to:

Conduct effective time series data analysis on large-scale energy datasets. Implement data preprocessing and cleaning techniques for real-world energy data. Utilize advanced visualization techniques to uncover insights. Build and evaluate predictive models for solar energy forecasting. Interpret model results and assess feature importance for improved explainability.

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