Building Energy Analysis
visualization and analysis of simulated building energy data
Summary
- Conducted thorough data analysis, cleaning, and pre-processing using pandas library, resulting in improved data quality and reduced errors.
- leveraged Matplotlib and Seaborn’s functionalities to create visually compelling and informative charts that enhanced the overall understanding of the underlying data.
- Utilized K-means Clustering on electrical meter data to identify daily load profiles and implemented a k-nearest neighbor regression model to accurately predict energy consumption with a MAPE of 6.59%.
- Implemented Random Forest model on ASHRAE thermal dataset, enabling the analysis of thermal sensation and identify key factors influencing human comfort with feature importance plot.