Integrating LDA clustering and autoencoder-based transfer learning for thermal comfort prediction

Key Points

• Thermal comfort predictions improved with LDA clustering and autoencoder methods, enhancing accuracy.
• A robust system utilizing LDA clustering produced a performance increase, achieving a 20% reduction in error.
• Analysis integrates thermal comfort data through advanced algorithms like autoencoder and LDA clustering.
• These findings support the potential for developing smarter environmental control systems using predictive modeling.