Published in September 2023, this report by H. Liang and a team of experts from various organizations, including Broad Air-Conditioning Co., Ltd., examines the critical problem of indoor air pollution and its significant health implications. With individuals spending a considerable amount of time indoors, accurate prediction of indoor air quality is essential. The authors propose a neural network model, leveraging the Informer model integrated with a data-correlation feature extractor based on MLP (Multi-Layer Perceptron), to predict indoor air quality effectively. The report emphasizes the urgent issue of indoor air pollution and proposes am AI-based prediction model. By leveraging advanced AI techniques, this study presents a robust approach to enhancing indoor air quality management, contributing significantly to the development of smart, healthy, and sustainable living environments.

Keywords: Artificial Intelligence (AI), HVAC & Indoor Air Quality (IAQ), Public Health, Deep Learning, Data Correlation