Spring 2024

This project is an Entrepreneurially Minded Course-based Undergraduate Research Experience (EM–CURE) assignment from the course ECE 4610/6610: Electrical Energy Conversion.

The primary goals are :

• Data Processing (Verification & Cleaning)

Identify Corrupted Data: Detect and document missing or erroneous entries in the time-series datasets (energy consumption for three different types of commercial buildings).

Mitigation Strategy: Evaluate different approaches (e.g., zero-filling, row deletion, interpolation) and justify a best practice.

Linear Interpolation: Correct corrupted data points by filling them using neighboring values.

• Data Visualization & Analysis

Plot Selection: Choose three plot types (e.g., pie charts, line plots, bar charts) to highlight different aspects of the energy consumption data.

Plot Generation: Create nine plots (three for each building type) using Python.

Analysis & Insights: Compare the differences in the buildings’ energy usage and discuss what might cause or justify these variations.

By completing these steps, students practice core research and data science skills—ensuring dataset integrity before and after experiments/simulations, and using visual analytics to derive meaningful insights into building energy consumption patterns.