Machine Learning for Daylight Prediction
Patrick Duhirwe
Data Analyst
ML Engineer
Python
R
TensorFlow
Machine Learning Model Development: Developing and training CNN, GRU, and CNN + GRU models for predicting indoor illuminance.
Model Performance Optimization and Evaluation: Tuning model parameters and evaluating performance using metrics like R2, RMSE, and MAE.
Generalization and Sensor Grouping: Testing models' generalization on unseen data and grouping illuminance sensors for analysis.
Computational Efficiency Analysis: Comparing models in terms of training time, prediction speed, and model size.
