Recommender systems and reinforcement learning for building control and occupant interaction: A text-mining driven review of scientific literature
This repository contains the data and code for paper:
Wenhao Zhang, Matias Quintana, Clayton Miller Recommender systems and reinforcement learning for building control and occupant interaction: A text-mining driven review of scientific literature.
The indoor environment significantly impacts human health and well-being, enhancing health and reducing energy consumption in these settings is a central research focus. With the advancement of information and communication technology (ICT), recommendation systems and reinforcement learning have emerged as a promising approach for inducing behavioral changes to improve the indoor environment and building energy efficiency. This study aims to employ text-mining and Natural Language Processing (NLP) techniques to thoroughly examine the connections among these approaches within the built environment. The study analyzed around 27,000 articles from the ScienceDirect database, revealing extensive use of recommendation systems and reinforcement learning for space optimization, location recommendations, and personalized control suggestions. While these systems are broadly applied to specific content, their use in optimizing indoor environments and energy efficiency remains limited. This gap likely arises from the need for interdisciplinary knowledge and extensive sensor data. Traditional recommendation algorithms, including collaborative filtering, content-based, and knowledge-based methods, are commonly employed. However, the more complex challenges of optimizing indoor conditions and energy efficiency frequently depend on sophisticated machine learning techniques like reinforcement learning and deep learning. Furthermore, this review underscores the vast potential for broadening recommender system and reinforcement learning applications in building and indoor environments. Fields ripe for innovation include predictive maintenance, the selection of building materials, and the optimization of environments tailored for specific needs, such as sleep and productivity enhancements based on user feedback. The study also notes the limitations of the method in capturing subtle academic nuances. Future improvements could involve integrating and fine-tuning pertrained language models to better interpret complex texts.
Text and figures : CC-BY-4.0