Mesa Jiménez, J J (2021) Artificial intelligence for optimisation and demand side response in built environment. PhD thesis, Brunel University, UK.
Abstract
In recent years, data analytics and machine learning have become important for generating insights and creating competitive advantages across many industries. However, despite recent developments in analytics and machine learning technologies, as well as accessible tools to perform the implementation of such technologies, built environment is still a largely unexplored field, where many engineering operations remain manual. Recent advances in building management systems and data engineering have provided vast amount of data streams of all types of sensors around the built environment: comfort variables, assets, security installations, meteorological measurements, electricity demand, etc. This creates a wide range of opportunities to explore data and extract real value for various operational purposes. The aim of this Thesis is to develop a series of tools related for control and optimisation of built environment, from demand side response events prediction to building operations management. Equipped with the results of this work, building managers will be more prepared to respond to energy demand events, organise energy resources more efficiently and to acquire a proactive approach to system failures and system errors tractability. The pilot results of the thesis have been successfully implemented in industrial applications.
| Item Type: | Thesis (Doctoral) |
|---|---|
| Thesis advisor: | Yang, Q and Pisica, I |
| Uncontrolled Keywords: | building operations; built environment; competitive advantage; learning; operations management; security; sensors; failure; optimisation; machine learning |
| Index terms: | building management system, built environment, energy demand, industrial application, electricity demand, competitive advantage, energy resource, stream, building operation, manager, operations management, artificial intelligence, implementation, comfort, machine learning |
| Subjects: | contractual arrangements, water management, energy systems, innovation and technology management, occupational health and safety management, infrastructure and transport systems, practitioner, management, artificial intelligence, mechanical systems, market analysis |
| Topics: | Health and Safety, Urban Studies, Procurement, Roles and Professions, Digital Applications, Engineering Principles, Business Strategy, Research Practice, Sustainability |
| Descriptive scope: | 2 PC |
N.B. Descriptive scope is a count of how many of the five facets of empirical research are indicated by the words used in title, abstract and keywords. It is not intended as a judgement on the research; merely a count of the kind of word we would expect to indicate Phenomenon, Concepts, Theoretical framing, Empirical techniques, Analytical techniques. If all five are present, then a code of “5 PCTEA” will indicate this. If you feel the coding for this record is questionable, we welcome discussion around the terms we matched or the way we categorized them. The facet you would expect may not be coded, or a facet may be coded inappropriately. This can also bear on a larger question, of which facets should be treated as defining in construction management research. Please get in touch, and we will look at it. More details here