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Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment

Edited by
March 2024
264 pages
  • ISBN978-3-7258-0473-3 (Hardback)
  • ISBN978-3-7258-0474-0 (PDF)
https://doi.org/10.3390/books978-3-7258-0474-0
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This book is a reprint of the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment that was published in

Chemistry & Materials Science
Computer Science & Mathematics
Engineering
Environmental & Earth Sciences
Physical Sciences
Summary

Many advanced control methods have been proposed in the past to improve the operation of buildings. This reprint collected publications dealing with advanced control solutions implemented in existing buildings to improve their performance. Moreover, it gathers promising methodologies to facilitate the adoption of advanced control solutions in the building control industry. It includes virtual sensing for indoor air pollutants, novel models to predict indoor air temperature as well as building heating and cooling loads; and model-based controls, predictive controls, and predictive maintenance algorithms to optimize setpoint tracking and the operation of building heating and cooling systems.

Format
  • Hardback
License
© 2022 by the authors; CC BY-NC-ND license
Keywords
model predictive control; advanced controls; control-oriented models; energy efficiency; optimization; thermal comfort; energy flexibility; model-based control strategies; school buildings; measured data; predictive maintenance; faults detection and diagnosis; chilled water system; commercial buildings; Industry 4.0; Quality 4.0; data-driven analysis; energy consumption; data-driven; prediction; building; heating load; cooling load; optimization; building; HVAC; control; energy efficiency; faults; predictive maintenance; chilled water system; commercial buildings; industry 4.0; quality 4.0; survey; faults; frequencies; advanced controls; energy efficiency; model-based controls; free cooling; chiller sequencing; temperature reset strategies; predictive maintenance; Industry 4.0; Quality 4.0; decision tree algorithm; chilled water system; HVAC; commercial buildings; industrial engineering; engineering management; indoor temperature; HVAC; machine learning; deep learning; educational building; machine learning; deep learning; virtual sensing; LSTM; IAQ; monitoring; advanced controls; data analytics; decarbonization; flexibility; model predictive control; building operation; n/a