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

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A special issue of Buildings (ISSN 2075-5309). This special issue belongs to the section "Building Energy, Physics, Environment, and Systems".

Deadline for manuscript submissions: closed (31 May 2023) | Viewed by 26486

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Special Issue Editor

Dr. Etienne Saloux

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Guest Editor

CanmetENERGY in Varennes, Natural Resources Canada, Ottawa, ON, Canada
Interests: energy efficiency in building; energy conversion systems; thermal engineering; thermodynamics; exergy; advanced controls, model-based predictive control; renewable energy

Special Issue Information

Dear Colleagues,

Despite the development of increasingly efficient technologies and the increasing amount of available data from building automation systems and connected devices, buildings are still far from reaching their performance potentials due to inadequate system controls and suboptimal operation sequences. Model-based Predictive Control (MPC) and other advanced control methods such as model-based controls are widely acknowledged as effective solutions for improving building operation. Model-based controls rely on control-oriented models to make informed decisions. MPC relies on a control-oriented model used along with information forecasts such as weather or occupancy to predict building behaviour hours ahead and optimize heating and cooling system operations accordingly. Although MPC and advanced controls for buildings have been widely investigated in the past, practical solutions targeting field implementation remain relatively rare. The aim of this Special Issue is to collect and disseminate knowledge about the following: (a) experiences with practical MPC strategies and advanced controls implemented in actual buildings to improve performance; (b) and promising methodologies to facilitate the adoption of MPC and advanced controls in building control industry. Applications targeting the optimization of energy efficiency, peak demand, flexibility and total cost will be considered in addition to indoor air quality and thermal comfort.

Dr. Etienne Saloux
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Buildings is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

buildings
building energy systems
model predictive control
advanced controls
control-oriented models
load management
energy efficiency
flexibility
thermal comfort
indoor air quality

Published Papers (12 papers)

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Editorial

Jump to: Research, Review

5 pages, 430 KiB

Open AccessEditorial

Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment: An Overview of the Special Issue

by Etienne Saloux

Buildings 2024, 14(2), 534; https://doi.org/10.3390/buildings14020534 - 17 Feb 2024

Viewed by 545

Abstract

This paper summarizes the results of a Special Issue focusing on the practical applications of model predictive control and other advanced control methods in the built environment. This Special Issue contains eleven publications and deals with various topics such as the virtual sensing of indoor air pollutants and prediction models for indoor air temperature and building heating and cooling loads, as well as local and supervisory control strategies. The last three publications tackle the predictive maintenance of chilled water systems. Most of these publications are field demonstrations of advanced control solutions or promising methodologies to facilitate the adoption of such control strategies, and they deal with existing buildings. The Special Issue also contains two review papers that provide a comprehensive overview of practical challenges, opportunities, and solutions to improve building operations. This article concludes with a discussion of the perspectives of advanced controls in the built environment and the increasing importance of data-driven solutions. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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Research

Jump to: Editorial, Review

21 pages, 1557 KiB

Open AccessArticle

LSTM Deep Learning Models for Virtual Sensing of Indoor Air Pollutants: A Feasible Alternative to Physical Sensors

by Martin Gabriel and Thomas Auer

Buildings 2023, 13(7), 1684; https://doi.org/10.3390/buildings13071684 - 30 Jun 2023

Cited by 2 | Viewed by 1560

Abstract

Monitoring individual exposure to indoor air pollutants is crucial for human health and well-being. Due to the high spatiotemporal variations of indoor air pollutants, ubiquitous sensing is essential. However, the cost and maintenance associated with physical sensors make this currently infeasible. Consequently, this [...] Read more.

_{2}

, using a long short-term memory (LSTM) deep learning model. Several years of accumulated measurement data were employed to train the model, which predicts indoor air pollutant concentrations based on Building Management System (BMS) data (e.g., temperature, humidity, illumination, noise, motion, and window state) as well as meteorological and outdoor pollution data. A cross-validation scheme and hyperparameter optimization were utilized to determine the best model parameters and evaluate its performance using common evaluation metrics (R

^{2}

, mean absolute error (MAE), root mean square error (RMSE)). The results demonstrate that the LSTM model can effectively replace physical indoor air pollutant sensors in the examined room, with evaluation metrics indicating a strong correlation in the testing set (MAE; CO

_{2}

: 15.4 ppm, PM

_{2.5}

: 0.3

μ

g/m

^{3}

, VOC: 20.1 IAQI; R

^{2}

; CO

_{2}

: 0.47, PM

_{2.5}

: 0.88, VOC:0.87). Additionally, the transferability of the model to other rooms was tested, with good results for CO₂ and mixed results for VOC and particulate matter (MAE; CO

_{2}

: 21.9 ppm, PM

_{2.5}

: 0.3

μ

g/m

^{3}

, VOC: 52.7 IAQI; R

^{2}

; CO

_{2}

: 0.45, PM

_{2.5}

: 0.09, VOC:0.13). Despite these mixed results, they hint at the potential for a more broadly applicable approach to virtual sensing of indoor air pollutants, given the incorporation of more diverse datasets, thereby offering the potential for real-time occupant exposure monitoring and enhanced building operations. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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24 pages, 5799 KiB

Open AccessArticle

Applicability of Deep Learning Algorithms for Predicting Indoor Temperatures: Towards the Development of Digital Twin HVAC Systems

by Pooria Norouzi, Sirine Maalej and Rodrigo Mora

Buildings 2023, 13(6), 1542; https://doi.org/10.3390/buildings13061542 - 16 Jun 2023

Cited by 5 | Viewed by 2007

Abstract

The development of digital twins leads to the pathway toward intelligent buildings. Today, the overwhelming rate of data in buildings carries a high amount of information that can provide an opportunity for a digital representation of the buildings and energy optimization strategies in the Heating, Ventilation, and Air Conditioning (HVAC) systems. To implement a successful energy management strategy in a building, a data-driven approach should accurately forecast the HVAC features, in particular the indoor temperatures. Accurate predictions not only increase thermal comfort levels, but also play a crucial role in saving energy consumption. This study aims to investigate the capabilities of data-driven approaches and the development of a model for predicting indoor temperatures. A case study of an educational building is considered to forecast indoor temperatures using machine learning and deep learning algorithms. The algorithms’ performance is evaluated and compared. The important model parameters are sorted out before choosing the best architecture. Considering real data, prediction models are created for indoor temperatures. The results reveal that all the investigated models are successful in predicting indoor temperatures. Hence, the proposed deep neural network model obtained the highest accuracy with an average RMSE of 0.16 °C, which renders it the best candidate for the development of a digital twin. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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19 pages, 5862 KiB

Open AccessArticle

Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Methodological Framework

by Malek Almobarek, Kepa Mendibil and Abdalla Alrashdan

Buildings 2023, 13(2), 497; https://doi.org/10.3390/buildings13020497 - 12 Feb 2023

Cited by 3 | Viewed by 2146

Abstract

Predictive maintenance is considered as one of the most important strategies for managing the utility systems of commercial buildings. This research focused on chilled water system (CWS) components and proposed a methodological framework to build a comprehensive predictive maintenance program in line with Industry 4.0/Quality 4.0 (PdM 4.0). This research followed a systematic literature review (SLR) study that addressed two research questions about the mechanism for handling CWS faults, as well as fault prediction methods. This research rectified the associated research gaps found in the SLR study, which were related to three points; namely fault handling, fault frequencies, and fault solutions. A framework was built based on the outcome of an industry survey study and contained three parts: setup, machine learning, and quality control. The first part explained the three arrangements required for preparing the framework. The second part proposed a decision tree (DT) model to predict CWS faults and listed the steps for building and training the model. In this part, two DT algorithms were proposed, C4.5 and CART. The last part, quality control, suggested managerial steps for controlling the maintenance program. The framework was implemented in a university, with encouraging outcomes, as the prediction accuracy of the presented prediction model was more than 98% for each CWS component. The DT model improved the fault prediction by more than 20% in all CWS components when compared to the existing control system at the university. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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24 pages, 6534 KiB

Open AccessArticle

Data-Driven Model-Based Control Strategies to Improve the Cooling Performance of Commercial and Institutional Buildings

by Etienne Saloux and Kun Zhang

Buildings 2023, 13(2), 474; https://doi.org/10.3390/buildings13020474 - 9 Feb 2023

Cited by 6 | Viewed by 1573

Abstract

The increasing amount of operational data in buildings opens up new methods for improving building performance through advanced controls. Although predictive control has been widely investigated in the literature, field demonstrations still remain rare. Alternatively, model-based controls can provide similar improvement while being easier to implement in real buildings. This paper investigates three data-driven model-based control strategies to improve the cooling performance of commercial and institutional buildings: (a) chiller sequencing, (b) free cooling, and (c) supply air temperature reset. These energy efficiency measures are applied to an existing commercial building in Canada with data from summer 2020 and 2021. The impact of each measure is individually assessed, as well as their combined effects. The results show that all three of the measures together reduce building cooling energy by 12% and cooling system electric energy by 33%. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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15 pages, 2324 KiB

Open AccessArticle

Fault Types and Frequencies in Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: An Industry Survey

by Malek Almobarek, Kepa Mendibil, Abdalla Alrashdan and Sobhi Mejjaouli

Buildings 2022, 12(11), 1995; https://doi.org/10.3390/buildings12111995 - 16 Nov 2022

Cited by 4 | Viewed by 1740

Abstract

Predictive Maintenance 4.0 (PdM 4.0) showed a highly positive impact on chilled water system (CWS) maintenance. This research followed the recommendations of a systematic literature review (SLR), which was performed on PdM 4.0 applications for CWS at commercial buildings. Per the SLR, and to start making an excellent PdM 4.0 program, the faults and their frequencies must be identified. Therefore, this research constructed an industry survey, which went through a pilot study, and then shared it with 761 maintenance officers in different commercial buildings. The first goal of this survey is to verify the faults reported by SLR, explore more faults, and suggest a managerial solution for each fault. The second goal is to determine the minimum and maximum frequencies of faults occurrence, while the third goal is to verify selected operational parameters, in which their data can be used in smart buildings applications. A total of 304 responses are considered in this study, which identified additional faults and provided faults solutions for all CWS components. Based on the survey outcomes, justifiable frequencies are proposed, which can be used in creating the dataset of any machine learning model, and then to control the CWS performance. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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22 pages, 10003 KiB

Open AccessArticle

Cascaded Control for Building HVAC Systems in Practice

by Chris Price, Deokgeun Park and Bryan P. Rasmussen

Buildings 2022, 12(11), 1814; https://doi.org/10.3390/buildings12111814 - 28 Oct 2022

Cited by 2 | Viewed by 2085

Abstract

Actuator hunting is a widespread and often neglected problem in the HVAC field. Hunting is typically characterized by sustained or intermittent oscillations, and can result in decreased efficiency, increased actuator wear, and poor setpoint tracking. Cascaded control loops have been shown to effectively linearize system dynamics and reduce the prevalence of hunting. This paper details the implementation of cascaded control architectures for Air Handling Unit chilled water valves at three university campus buildings. A framework for implementation the control in existing Building Automation software is developed that requires only a single line of additional code. Results gathered for more than a year show that cascaded control not only eliminates hunting in control loops with documented hunting issues, but provides better tracking and more consistent performance during all seasons. A discussion of efficiency losses due to hunting behavior is presented and illustrated with comparative data. Furthermore, an analysis of cost savings from implementing cascaded chilled water valve control is presented. Field tests show 2.2–4.4% energy savings, with additional potential savings from reduced operational costs (i.e., maintenance and controller retuning). Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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18 pages, 8150 KiB

Open AccessArticle

Buildings’ Heating and Cooling Load Prediction for Hot Arid Climates: A Novel Intelligent Data-Driven Approach

by Kashif Irshad, Md. Hasan Zahir, Mahaboob Sharief Shaik and Amjad Ali

Buildings 2022, 12(10), 1677; https://doi.org/10.3390/buildings12101677 - 12 Oct 2022

Cited by 6 | Viewed by 1780

Abstract

An important aspect in improving the energy efficiency of buildings is the effective use of building heating and cooling load prediction models. A lot of studies have been undertaken in recent years to anticipate cooling and heating loads. Choosing the most effective input parameters as well as developing a high-accuracy forecasting model are the most difficult and important aspects of prediction. The goal of this research is to create an intelligent data-driven load forecast model for residential construction heating and cooling load intensities. In this paper, the shuffled shepherd red deer optimization linked self-systematized intelligent fuzzy reasoning-based neural network (SSRD-SsIF-NN) is introduced as a novel intelligent data-driven load prediction method. To test the suggested approaches, a simulated dataset based on the climate of Dhahran, Saudi Arabia will be employed, with building system parameters as input factors and heating and cooling loads as output results for each system. The simulation of this research is executed using MATLAB software. Finally, the theoretical and experimental results demonstrate the efficacy of the presented techniques. In terms of Mean Square Error (MSE), Root Mean Square Error (RMSE), Regression (R) values, Mean Absolute Error (MAE), coefficient of determination (R2), and other metrics, their prediction performance is compared to that of other conventional methods. It shows that the proposed method has achieved the finest performance of load prediction compared with the conventional methods. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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21 pages, 8106 KiB

Open AccessArticle

Model-Based Control Strategies to Enhance Energy Flexibility in Electrically Heated School Buildings

by Navid Morovat, Andreas K. Athienitis, José Agustín Candanedo and Benoit Delcroix

Buildings 2022, 12(5), 581; https://doi.org/10.3390/buildings12050581 - 30 Apr 2022

Cited by 9 | Viewed by 2168

Abstract

This paper presents a general methodology to model and activate the energy flexibility of electrically heated school buildings. The proposed methodology is based on the use of archetypes of resistance–capacitance thermal networks for representative thermal zones calibrated with measured data. Using these models, predictive control strategies are investigated with the aim of reducing peak demand in response to grid requirements and incentives. A key aim is to evaluate the potential of shifting electricity use in different archetype zones from on-peak hours to off-peak grid periods. Key performance indicators are applied to quantify the energy flexibility at the zone level and the school building level. The proposed methodology has been implemented in an electrically heated school building located in Québec, Canada. This school has several features (geothermal heat pumps, hydronic radiant floors, and energy storage) that make it ideal for the purpose of this study. The study shows that with proper control strategies through a rule-based approach with near-optimal setpoint profiles, the building’s average power demand can be reduced by 40% to 65% during on-peak hours compared to a typical profile. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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25 pages, 10707 KiB

Open AccessFeature PaperArticle

Comparison of Model Complexities in Optimal Control Tested in a Real Thermally Activated Building System

by Javier Arroyo, Fred Spiessens and Lieve Helsen

Buildings 2022, 12(5), 539; https://doi.org/10.3390/buildings12050539 - 23 Apr 2022

Cited by 10 | Viewed by 3193

Abstract

Building predictive control has proven to achieve energy savings and higher comfort levels than classical rule-based controllers. The choice of the model complexity needed to be used in model-based optimal control is not trivial, and a wide variety of model types is implemented in the scientific literature. This paper shares practical aspects of implementing different control-oriented models for model predictive control in a building. A real thermally activated test building is used to compare the white-, grey-, and black-box modeling paradigms in prediction and control performance. The experimental results obtained in our particular case reveal that there is not a significant correlation between prediction and control performance and highlight the importance of modeling the heat emission system based on physics. It is also observed that most of the complexity of the physics-based model arises from the building envelope while this part of the building is the most sensitive to weather forecast uncertainty. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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Review

Jump to: Editorial, Research

29 pages, 2393 KiB

Open AccessReview

A Critical Perspective on Current Research Trends in Building Operation: Pressing Challenges and Promising Opportunities

by Etienne Saloux, Kun Zhang and José A. Candanedo

Buildings 2023, 13(10), 2566; https://doi.org/10.3390/buildings13102566 - 11 Oct 2023

Cited by 2 | Viewed by 1378

Abstract

Despite the development of increasingly efficient technologies and the ever-growing amount of available data from Building Automation Systems (BAS) and connected devices, buildings are still far from reaching their performance potential due to inadequate controls and suboptimal operation sequences. Advanced control methods such as model-based controls or model-based predictive controls (MPC) are widely acknowledged as effective solutions for improving building operation. Although they have been well-investigated in the past, their widespread adoption has yet to be reached. Based on our experience in this field, this paper aims to provide a broader perspective on research trends on advanced controls in the built environment to researchers and practitioners, as well as to newcomers in the field. Pressing challenges are explored, such as inefficient local controls (which must be addressed in priority) and data availability and quality (not as good as expected, despite the advent of the digital era). Other major hurdles that slow down the large-scale adoption of advanced controls include communication issues with BAS and lack of guidelines and standards tailored for controls. To encourage their uptake, cost-effective solutions and successful case studies are required, which need to be further supported by better training and engagement between the industry and research communities. This paper also discusses promising opportunities: while building modelling is already playing a critical role, data-driven methods and data analytics are becoming a popular option to improve buildings controls. High-performance local and supervisory controls have emerged as promising solutions. Energy flexibility appears instrumental in achieving decarbonization targets in the built environment. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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29 pages, 2470 KiB

Open AccessFeature PaperReview

Predictive Maintenance 4.0 for Chilled Water System at Commercial Buildings: A Systematic Literature Review

by Malek Almobarek, Kepa Mendibil and Abdalla Alrashdan

Buildings 2022, 12(8), 1229; https://doi.org/10.3390/buildings12081229 - 13 Aug 2022

Cited by 7 | Viewed by 4055

Abstract

Predictive maintenance plays an important role in managing commercial buildings. This article provides a systematic review of the literature on predictive maintenance applications of chilled water systems that are in line with Industry 4.0/Quality 4.0. The review is based on answering two research questions about understanding the mechanism of identifying the system’s faults during its operation and exploring the methods that were used to predict these faults. The research gaps are explained in this article and are related to three parts, which are faults description and handling, data collection and frequency, and the coverage of the proposed maintenance programs. This article suggests performing a mixed method study to try to fill in the aforementioned gaps. Full article

(This article belongs to the Special Issue Practical Applications of Model Predictive Control and Other Advanced Control Methods in the Built Environment)

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