Developing a Reliable Shallow Supervised Learning for Thermal Comfort Using Multiple ASHRAE Databases

The artificial intelligence (AI) system faces the challenge of insufficient training datasets and the risk of an uncomfortable user experience during the data gathering and learning process. The unreliable training data leads to overfitting and poor system performance which will result in wasting operational energy. This work introduces a reliable data set for training the AI subsystem for thermal comfort. The most reliable current training data sets for thermal comfort are ASHRAE RP-884 and ASHRAE Global Thermal Comfort Database II, but the direct use of these data for learning will give a poor learning result of less than 60% accuracy. This article presents the algorithm for data filtering and semantic data augmentation for the multiple ASHRAE databases for the supervised learning process. The result was verified with the visual psychrometric chart method that can check for overfitting and verified by developing the Internet of Things (IoT) control system for residential usage based on shallow supervised learning. The AI system was a wide artificial neural network (ANN) which is simple enough to be implemented in a local node. The filtering and semantic augmentation method can increase the accuracy to 96.1%. The control algorithm that was developed based on the comfort zone identification can increase the comfort acknowledgement by 6.06% leading to energy saving for comfort. This work can contribute to 717.2 thousand tonnes of CO₂ equivalent per year which is beneficial for a more sustainable thermal comfort system and the development of a reinforced learning system for thermal comfort.