Application of Artificial Neural Network Analysis in Defining the Relationship Between Indoor Environment Quality and Occupant Productivity

Humans spend most of their time indoors and the majority of the world’s population lives in urban areas and spend most of their time in an office environment. There is a vast literature on indoor environment quality and its effects on occupant productivity. The main purpose of this paper is to highlight the development and application of Artificial Neural Network (ANN) model to define the relationship between indoor environment quality factors and occupant productivity. ANN represents a computational technique based on complex brain computation. It is highly applicable to non-linear functions and complex patterns. ANN has been applied in various disciplines like finance, biology, and bioinformatics to solve a wide range of complex non-linear problems. This model is developed to identify non-linear relationships between six indoor environment quality factors and occupant productivity in an office building in Qatar. The physical environment data was collected using 90 sensors, and occupant productivity was reported using online survey. This paper contributes to the research area of building performance and environment and data analytics in built environment research. It provides new data analysis method to establish a mathematical relationship between indoor environment and occupant productivity. It highlights a new dimension in data analytics in the built environment research.