Abstract Generative Adversarial Networks (GANs) are a type of deep neural network that
have achieved many state-of-the-art results for generative tasks. GANs can be useful in the …

The existence of class imbalance in a dataset can greatly bias the classifier towards majority
classification. This discrepancy can pose a serious problem for deep learning models, which …

Conventional thermal preference prediction in buildings has limitations due to the difficulty in
capturing all environmental and personal factors. New model features can improve the …

Abstract Machine learning-based human thermal comfort prediction is becoming
increasingly popular as artificial intelligence (AI) technologies advance. Human skin …

Building energy prediction and management has become increasingly important in recent
decades, driven by the growth of Internet of Things (IoT) devices and the availability of more …

Abstract Cohort Comfort Models (CCM) are introduced as a technique for creating a
personalized thermal prediction for a new building occupant without the need to collect large …

Personal thermal comfort models are a paradigm shift in predicting how building occupants
perceive their thermal environment. Previous work has critical limitations related to the …

We present a new method to create spatial data using a generative adversarial network
(GAN). Our contribution uses coarse and widely available geospatial data to create maps of …

The ever-changing data science landscape is fueling innovation in the built environment
context by providing new and more effective means of converting large raw data sets into …

Abstract Generative Adversarial Network (GAN) is widely used in many generative
problems, including in spatial information sciences and urban systems. The data generated …