Intelligent Environment Design for Indoor Spaces Based on Perception and Behavior Correlation

Abstract

With the progress of social economy, higher demands have been presented for indoor environment. Intelligent design has become a focus. A Genetic Algorithm-Back Propagation Neural Network model is constructed ground on the association between perception and behavior, combined with the Predicted Mean Vote index. Six factors affecting the Predicted Mean Vote index are analyzed and predicted. However, there are inherent flaws in the Back Propagation Neural Network. Therefore, combining Genetic Algorithm for improvement, a optimized model is built. It had faster convergence speed than the unimproved model. The difference of Predicted Mean Vote was small, with better model fitting effect. The overall model error remained around 0.01, with a maximum error of only 0.022. The model had higher Accuracy, Precision, and F1-score values compared with other models, with values of 97.89%, 96.15%, and 0.896. From the results, it has better generalization ability, which can accurately predict indoor temperature, achieving intelligent control. The model proposed in the study achieves intelligent design of indoor comfort by controlling temperature, providing a reliable foundation for further improving indoor intelligence in subsequent research.