Occupant-Facade interaction: a review and classification scheme

The interest in occupant interaction with building controls and automation systems is growing due to the wider availability of embedded sensing devices and automated or intelligent building components that can integrate building control strategies with occupant-centred data and lead to greater occupant satisfaction and reduction in energy consumption. An area of particular interest is the interaction strategies between occupants and the so called automated facades, such as dynamic shading devices and switchable glazing. Occupant-Facade interactions are often disruptive and source of dissatisfaction because of conflicts between competing requirements, e.g. energy-efficiency and indoor environmental quality. To solve these conflicts, expertise from several disciplines is required, including Behavioural Science and Building Physics, but the absence of common research frameworks impedes knowledge transfer between different fields of expertise. This paper reviews existing multi-disciplinary research on occupant interaction with facades, buildings and automation systems and provides a new classification scheme of Occupant-Facade interaction. The scheme is based on an extensive review of interactive scenarios between occupants and facades that are summarised in this paper. The classification scheme was found to be successful in: 1) capturing the multidisciplinary nature of interactive scenarios by clarifying relationships between components; 2) identifying similarities and characteristics among interactive scenarios; 3) understanding research gaps. The classification scheme proposed in this paper has the potential to be a useful tool for the multi-disciplinary research community in this field. The review also showed that more research is needed to characterise the holistic and multi-disciplinary effect of occupant interaction with intelligent building components.EPSRC Doctoral Training Accoun

Machine learning for smart building applications: Review and taxonomy

© 2019 Association for Computing Machinery. The use of machine learning (ML) in smart building applications is reviewed in this article. We split existing solutions into two main classes: occupant-centric versus energy/devices-centric. The first class groups solutions that use ML for aspects related to the occupants, including (1) occupancy estimation and identification, (2) activity recognition, and (3) estimating preferences and behavior. The second class groups solutions that use ML to estimate aspects related either to energy or devices. They are divided into three categories: (1) energy profiling and demand estimation, (2) appliances profiling and fault detection, and (3) inference on sensors. Solutions in each category are presented, discussed, and compared; open perspectives and research trends are discussed as well. Compared to related state-of-the-art survey papers, the contribution herein is to provide a comprehensive and holistic review from the ML perspectives rather than architectural and technical aspects of existing building management systems. This is by considering all types of ML tools, buildings, and several categories of applications, and by structuring the taxonomy accordingly. The article ends with a summary discussion of the presented works, with focus on lessons learned, challenges, open and future directions of research in this field