Machine Learning for Daylight Prediction
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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Posted Feb 23, 2024
This project uses CNN and GRU models to predict indoor lighting from daylight, aiming for energy efficiency and enhanced environmental design.
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