Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems

Yang, Shiyu; Wan, Man Pun; Ng, Bing Feng; Dubey, Swapnil; Henze, Gregor P.; Chen, Wanyu; Baskaran, K.

doi:10.1016/j.apenergy.2021.117112

Cited by 54 publications

(22 citation statements)

References 33 publications

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“…Predictive control is one of the machine learning applications present in recent smart lighting research. Yang et al [275] utilizes predictive control in controlling smart lighting, air conditioning, ventilation, and shading in an integrated manner. Two test areas were ready to test the system, one where the proposed method was present and one room with the old system.…”

Section: ) Predictive Controlmentioning

confidence: 99%

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

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Smart lighting has become a universal smart product solution, with global revenues of up to US$5.9 billion by 2021. Six main factors drive the technology: light-emitting diode (LED) lighting, sensors, control, analytics, and intelligence. The Internet of things (IoT) concept with the end device, platform, and application layer plays an essential role in optimizing the advantages of LED lighting in the emergence of smart lighting. The ultimate aim of smart lighting research is to introduce low energy efficiency and high user comfort, where the latter is still in the infancy stage. This paper presents a systematic literature review (SLR) from a bird's eye view covering full-length research topics on smart lighting, including issues, implementation targets, technological solutions, and prospects. In addition to that, this paper also provides a detailed and extensive overview of emerging machine learning techniques as a key solution to overcome complex problems in smart lighting. A comprehensive review of improving user comfort is also presented, such as the methodology and taxonomy of activity recognition as a promising solution and user comfort metrics, including light utilization ratio, unmet comfort ratio, light to comfort ratio, power reduction rate, flickering perception, Kruithof's comfort curve, correlated color temperature, and relative mean square error. Finally, we discuss in-depth open issues and future challenges in increasing user comfort in smart lighting using activity recognition.

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Section: ) Predictive Controlmentioning

confidence: 99%

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

et al. 2022

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“…The control objectives of MPC are often expressed as penalties in the cost function J. The most frequently considered penalties in the objective functions include: (1) thermal comfort cost by penalizing the difference between the predictive and reference values of the Predicted Mean Vote (PMV) index [176], the deviation between the predictive and setpoint temperature [177] or the energy not supplied compared with the energy demand [178]; (2) the energy bill of the building [179]; (3) the energy consumption of the installed technologies [180]; (4) the carbon emissions calculated by the product of the carbon intensity and consumption of power [181]; (5) the nonrenewable energy proportion in the total load [7]. The MPC formulation is often deployed to find the optimal trade-off of the probable competitive objectives.…”

Section: Mpc Formulationmentioning

confidence: 99%

A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems

2022

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The Energy Management System (EMS) is an efficient technique to monitor, control and enhance the building performance. In the state-of-the-art, building performance analysis is separated into building simulation and control management: this may cause inaccuracies and extra operating time. Thus, a coherent framework to integrate building physics with various energy technologies and energy control management methods is highly required. This framework should be formed by simplified but accurate models of building physics and building energy technologies, and should allow for the selection of proper control strategies according to the control objectives and scenarios. Therefore, this paper reviews the fundamental mathematical modeling and control strategies to create such a framework. The mathematical models of (i) building physics and (ii) popular building energy technologies (renewable energy systems, common heating and cooling energy systems and energy distribution systems) are first presented. Then, it is shown how the collected mathematical models can be linked. Merging with two frequently used EMS strategies, namely rule-based and model predictive controls, is discussed. This work provides an extendable map to model and control buildings and intends to be a foundation for building researchers, designers and engineers.

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“…For instance, Ganesh et al [34] proposed a model-based indoor air quality(IAQ) MPC framework that can reduce the peak pollutant concentration by 31 % and shorten the exposure time by 48 % and 17.7 %, respectively. Yang et al [35] designed a multi-objective MPC platform for energy efficiency, indoor thermal comfort, and visual comfort. This could save 15.1–20.7 % of electricity usage while providing a better indoor environment.…”

Section: Introductionmentioning

confidence: 99%

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

et al. 2023

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Model predictive control for integrated control of air-conditioning and mechanical ventilation, lighting and shading systems

Cited by 54 publications

References 33 publications

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

Machine Learning Methods in Smart Lighting Toward Achieving User Comfort: A Survey

A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems

PANDEMIC: Occupancy driven predictive ventilation control to minimize energy consumption and infection risk

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