Data-Centric Approaches for Buildings Operation and Maintenance
A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".
Deadline for manuscript submissions: closed (10 July 2023) | Viewed by 8467
Special Issue Editor
Interests: reliability based maintenance and safety of structures and buildings; optimization; decision-making
Special Issues, Collections and Topics in MDPI journals
Special Issue Information
Dear Colleagues,
We have been given the honour of guest editing this Special Issue of Applied Sciences on data-centric approaches for building operation and maintenance (O&M). Maintaining user satisfaction, comfort, and safety; lowering building environmental impact; and ensuring the continuity of building services and activities are complex problems that require us to know and anticipate users’ needs and to adapt and regulate the responses of building systems to these needs. Data-centric approaches are gaining ground in O&M; they aim to maximise the value of buildings and reduce their operating costs through effective and intelligent analysis of data and knowledge about user behaviour and needs, as well as equipment performance. Recent advances in both data collection and processing (machine learning, deep learning, transfer learning) and computing (cloud, edge) mark the advent of new approaches to help define predictive and even prescriptive maintenance strategies.
In this Special Issue of Applied Sciences, we are looking for papers on challenges related to both the data collection and data processing stages, as well as understanding users’ behaviour and monitoring technical installations. We welcome papers that explore the topics of the optimal placement of single or multi-physics sensors, the fusion of synthetic and real time-series data, and the availability and scarcity of reliable data. Topics of interest also include the use of data-centric approaches for model-based predictive controls, automatic fault detection, and enhanced decisions and prescriptions for optimal maintenance execution and better coordination with operations. Although the main focus is on the use of artificial intelligence technologies, hybrid approaches with statistical models will also be considered as they allow for the discussion of key issues of generalisation and interpretability.
Both analytical and applied research will be considered for inclusion in the Special Issue. Review papers are also eligible for publication.
Dr. David Bigaud
Guest Editor
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Keywords
- building
- operation and maintenance
- data analytics
- AI technologies
- users
- equipment
- predictive maintenance
- prescriptive maintenance
- optimization
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