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.

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