OSU Campus Energy 3D Visualizer
Campus energy analysis, building comparisons, forecasting, and interactive 3D visualization.
Overview
Campus energy data is difficult to interpret as raw records. Engineers need ways to compare buildings and campuses, inspect historical trends, and explore future consumption.
A Python pipeline cleans and analyzes campus energy datasets, then transforms the analysis into lightweight data for an interactive 3D engineering visualization.
How it works
- Raw campus energy datasets enter a Python preprocessing pipeline.
- Analysis compares buildings and campuses, explores history, and forecasts consumption.
- Optimized exports supply the interactive 3D visualization.
Technical implementation
- Preprocessing prepares energy records for consistent analysis.
- Building and campus comparisons make differences in consumption easier to inspect.
- Lightweight exports separate data processing from interactive rendering.
Building comparisons require attention to context such as size and usage. The available description does not specify normalization choices, the forecasting model, or measured visualization performance.
Results
1st Place in the Advanced Track at Data I/O 2026. Connected a Python analysis pipeline with an interactive 3D visualization.
The resume documents historical analysis, forecasting, and optimized data export. Forecast error metrics, rendering benchmarks, and a validation protocol were not supplied.