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A science mapping approach based review of model predictive control for smart building operation management

    Jun Wang Affiliation
    ; Jianli Chen Affiliation
    ; Yuqing Hu Affiliation

Abstract

Model predictive control (MPC) for smart building operation management has become an increasingly popular and important topic in the academic community. Based on a total of 202 journal articles extracted from Web of Science, this study adopted a science mapping approach to conduct a holistic review of the literature sample. Chronological trends, contributive journal sources, active scholars, influential documents, and frequent keywords of the literature sample were identified and analyzed using science mapping. Qualitative discussions were also conducted explore in details the objectives and data requirements of MPC implementation, different modeling approaches, common optimization methods, and associated model constraints. Three research gaps and future directions of MPC were presented: the selection and establishment of MPC central model, the capability and security of processing massive data, and the involvement of human factors. This study provides a big picture of existing research on MPC for smart building operations and presents findings that can serve as comprehensive guides for researchers and practitioners to connect current research with future trends.

Keyword : model predictive control (MPC), building operation management, science mapping, literature review

How to Cite
Wang, J., Chen, J., & Hu, Y. (2022). A science mapping approach based review of model predictive control for smart building operation management. Journal of Civil Engineering and Management, 28(8), 661–679. https://doi.org/10.3846/jcem.2022.17566
Published in Issue
Oct 26, 2022
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This work is licensed under a Creative Commons Attribution 4.0 International License.

References

Afram, A., & Janabi-Sharifi, F. (2014). Theory and applications of HVAC control systems – A review of model predictive control (MPC). Building and Environment, 72, 343–355. https://doi.org/10.1016/j.buildenv.2013.11.016

Aftab, M., Chen, C., Chau, C. K., & Rahwan, T. (2017). Automatic HVAC control with real-time occupancy recognition and simulation-guided model predictive control in low-cost embedded system. Energy and Buildings, 154, 141–156. https://doi.org/10.1016/j.enbuild.2017.07.077

Andriamamonjy, A., Klein, R., & Saelens, D. (2019). Automated grey box model implementation using BIM and Modelica. Energy and Buildings, 188, 209–225. https://doi.org/10.1016/j.enbuild.2019.01.046

Arroyo, J., Spiessens, F., & Helsen, L. (2020). Identification of multi-zone grey-box building models for use in model predictive control. Journal of Building Performance Simulation, 13(4), 472–486. https://doi.org/10.1080/19401493.2020.1770861

Ascione, F., Bianco, N., De Stasio, C., Mauro, G. M., & Vanoli, G. P. (2016). Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort. Energy and Buildings, 111, 131–144. https://doi.org/10.1016/j.enbuild.2015.11.033

Auffenberg, F., Snow, S., Stein, S., & Rogers, A. (2017). A comfort-based approach to smart heating and air conditioning. ACM Transactions on Intelligent Systems and Technology (TIST), 9(3), 1–20. https://doi.org/10.1145/3057730

Avci, M., Erkoc, M., Rahmani, A., & Asfour, S. (2013). Model predictive HVAC load control in buildings using real-time electricity pricing. Energy and Buildings, 60, 199–209. https://doi.org/10.1016/j.enbuild.2013.01.008

Bacher, P., & Madsen, H. (2011). Identifying suitable models for the heat dynamics of buildings. Energy and Buildings, 43(7), 1511–1522. https://doi.org/10.1016/j.enbuild.2011.02.005

Bartolucci, L., Cordiner, S., Mulone, V., & Santarelli, M. (2019). Hybrid renewable energy systems: Influence of short term forecasting on model predictive control performance. Energy, 172, 997–1004. https://doi.org/10.1016/j.energy.2019.01.104

Ben-Nakhi, A. E., & Mahmoud, M. A. (2002). Energy conservation in buildings through efficient A/C control using neural networks. Applied Energy, 73(1), 5–23. https://doi.org/10.1016/S0306-2619(02)00027-2

Bianchini, G., Casini, M., Vicino, A., & Zarrilli, D. (2016). Demand-response in building heating systems: A Model Predictive Control approach. Applied Energy, 168, 159–170. https://doi.org/10.1016/j.apenergy.2016.01.088

Bianchini, G., Casini, M., Pepe, D., Vicino, A., & Zanvettor, G. G. (2019). An integrated model predictive control approach for optimal HVAC and energy storage operation in large-scale buildings. Applied Energy, 240, 327–340. https://doi.org/10.1016/j.apenergy.2019.01.187

Biyik, E., & Kahraman, A. (2019). A predictive control strategy for optimal management of peak load, thermal comfort, energy storage and renewables in multi-zone buildings. Journal of Building Engineering, 25, 100826. https://doi.org/10.1016/j.jobe.2019.100826

Blum, D. H., Arendt, K., Rivalin, L., Piette, M. A., Wetter, M., & Veje, C. T. (2019). Practical factors of envelope model setup and their effects on the performance of model predictive control for building heating, ventilating, and air conditioning systems. Applied Energy, 236, 410–425. https://doi.org/10.1016/j.apenergy.2018.11.093

Bourdeau, M., Zhai, q-X., Nefzaoui, E., Guo, X., & Chatellier, P. (2019). Modeling and forecasting building energy consumption: A review of data-driven techniques. Sustainable Cities and Society, 48, 101533. https://doi.org/10.1016/j.scs.2019.101533

Brager, G., & Baker, L. (2009). Occupant satisfaction in mixed-mode buildings. Building Research & Information, 37(4), 369–380. https://doi.org/10.1080/09613210902899785

Bünning, F., Huber, B., Heer, P., Aboudonia, A., & Lygeros, J. (2020). Experimental demonstration of data predictive control for energy optimization and thermal comfort in buildings. Energy and Buildings, 211, 109792. https://doi.org/10.1016/j.enbuild.2020.109792

Candanedo, J. A., & Athienitis, A. K. (2011). Predictive control of radiant floor heating and solar-source heat pump operation in a solar house. HVAC&R Research, 17(3), 235–256. https://doi.org/10.1080/10789669.2011.568319

Carli, R., Cavone, G., Ben Othman, S., & Dotoli, M. (2020). IOT based architecture for model predictive control of HVAC systems in smart buildings. Sensors, 20(3), 781. https://doi.org/10.3390/s20030781

Castilla, M., Álvarez, J. D., Berenguel, M., Rodríguez, F., Guzmán, J. L., & Pérez, M. (2011). A comparison of thermal comfort predictive control strategies. Energy and Buildings, 43(10), 2737–2746. https://doi.org/10.1016/j.enbuild.2011.06.030

Chaudhuri, T., Soh, Y. C., Li, H., & Xie, L. (2019). A feedforward neural network based indoor-climate control framework for thermal comfort and energy saving in buildings. Applied Energy, 248, 44–53. https://doi.org/10.1016/j.apenergy.2019.04.065

Chen, C. (2006). CiteSpace II: Detecting and visualizing emerging trends and transient patterns in scientific literature. Journal of the American Society for Information Science and Technology, 57(3), 359–377. https://doi.org/10.1002/asi.20317

Chen, C. (2017). Science mapping: a systematic review of the literature. Journal of Data and Information Science, 2(2), 1–40. https://doi.org/10.1515/jdis-2017-0006

Chen, C., & Song, M. (2019). Visualizing a field of research: A methodology of systematic scientometric reviews. PLoS ONE, 14(10), e0223994. https://doi.org/10.1371/journal.pone.0223994

Chen, C., Wang, J., Heo, Y., & Kishore, S. (2013). MPC-based appliance scheduling for residential building energy management controller. IEEE Transactions on Smart Grid, 4(3), 1401–1410. https://doi.org/10.1109/TSG.2013.2265239

Chen, X., Wang, Q., & Srebric, J. (2015). Model predictive control for indoor thermal comfort and energy optimization using occupant feedback. Energy and Buildings, 102, 357–369. https://doi.org/10.1016/j.enbuild.2015.06.002

Chen, J., Augenbroe, G., & Song, X. (2018). Lighted-weighted model predictive control for hybrid ventilation operation based on clusters of neural network models. Automation in Construction, 89, 250–265. https://doi.org/10.1016/j.autcon.2018.02.014

Chen, Y., & Hu, M. (2019). Swarm intelligence–based distributed stochastic model predictive control for transactive operation of networked building clusters. Energy and Buildings, 198, 207–215. https://doi.org/10.1016/j.enbuild.2019.06.010

Cheung, T., Schiavon, S., Parkinson, T., Li, P., & Brager, G. (2019). Analysis of the accuracy on PMV–PPD model using the ASHRAE Global Thermal Comfort Database II. Building and Environment, 153, 205–217. https://doi.org/10.1016/j.buildenv.2019.01.055

Cigler, J., Prívara, S., Váňa, Z., Žáčeková, E., & Ferkl, L. (2012). Optimization of predicted mean vote index within model predictive control framework: Computationally tractable solution. Energy and Buildings, 52, 39–49. https://doi.org/10.1016/j.enbuild.2012.05.022

Cipresso, P., Giglioli, I. A. C., Raya, M. A., & Riva, G. (2018). The past, present, and future of virtual and augmented reality research: A network and cluster analysis of the literature. Frontiers in Psychology, 9, 2086. https://doi.org/10.3389/fpsyg.2018.02086

Corbin, C. D., Henze, G. P., & May-Ostendorp, P. (2013). A model predictive control optimization environment for real-time commercial building application. Journal of Building Performance Simulation, 6(3), 159–174. https://doi.org/10.1080/19401493.2011.648343

Craparo, E., Karatas, M., & Singham, D. I. (2017). A robust optimization approach to hybrid microgrid operation using ensemble weather forecasts. Applied Energy, 201, 135–147. https://doi.org/10.1016/j.apenergy.2017.05.068

Crawley, D. B., Lawrie, L. K., Winkelmann, F. C., Buhl, W. F., Huang, Y. J., Pedersen, C. O., Strand, R. K., Liesen, R. J., Fisher, D. E., Witte, M. J., & Glazer, J. (2001). EnergyPlus: Creating a new-generation building energy simulation program. Energy and Buildings, 33(4), 319–331. https://doi.org/10.1016/S0378-7788(00)00114-6

De Coninck, R., Magnusson, F., Åkesson, J., & Helsen, L. (2016). Toolbox for development and validation of grey-box building models for forecasting and control. Journal of Building Performance Simulation, 9(3), 288–303. https://doi.org/10.1080/19401493.2015.1046933

De Rosa, M., Brennenstuhl, M., Andrade Cabrera, C., Eicker, U., & Finn, D. P. (2019). An iterative methodology for model complexity reduction in residential building simulation. Energies, 12(12), 2448. https://doi.org/10.3390/en12122448

Deb, K., Anand, A., & Joshi, D. (2002). A computationally efficient evolutionary algorithm for real-parameter optimization. Evolutionary Computation, 10(4), 371–395. https://doi.org/10.1162/106365602760972767

Dobbs, J. R., & Hencey, B. M. (2014). Model predictive HVAC control with online occupancy model. Energy and Buildings, 82, 675–684. https://doi.org/10.1016/j.enbuild.2014.07.051

Dong, B., & Lam, K. P. (2014). A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. Building Simulation, 7(1), 89–106. https://doi.org/10.1007/s12273-013-0142-7

Fanger, P. O. (1970). Thermal comfort. Analysis and applications in environmental engineering. Danish Technical Press.

Felizardo, K. R., Salleh, N., Martins, R. M., Mendes, E., MacDonell, S. G., & Maldonado, J. C. (2011, September). Using visual text mining to support the study selection activity in systematic literature reviews. In 2011 International Symposium on Empirical Software Engineering and Measurement (pp. 77–86). IEEE. https://doi.org/10.1109/ESEM.2011.16

Fernandes, E., Jung, J., & Prakash, A. (2016). Security analysis of emerging smart home applications. In 2016 IEEE symposium on Security and Privacy (SP) (pp. 636–654). IEEE. https://doi.org/10.1109/SP.2016.44

Fernandes, E., Rahmati, A., Eykholt, K., & Prakash, A. (2017). Internet of things security research: A rehash of old ideas or new intellectual challenges?. IEEE Security & Privacy, 15(4), 79–84. https://doi.org/10.1109/MSP.2017.3151346

Ferreira, P. M., Ruano, A. E., Silva, S., & Conceicao, E. Z. E. (2012). Neural networks based predictive control for thermal comfort and energy savings in public buildings. Energy and Buildings, 55, 238–251. https://doi.org/10.1016/j.enbuild.2012.08.002

Fong, K. F., Hanby, V. I., & Chow, T. T. (2006). HVAC system optimization for energy management by evolutionary programming. Energy and Buildings, 38(3), 220–231. https://doi.org/10.1016/j.enbuild.2005.05.008

Fontenot, H., & Dong, B. (2019). Modeling and control of building-integrated microgrids for optimal energy management – a review. Applied Energy, 254, 113689. https://doi.org/10.1016/j.apenergy.2019.113689

Foucquier, A., Brun, A., Faggianelli, G. A., & Suard, F. (2013). Effect of wall merging on a simplified building energy model: Accuracy vs number of equations. In 13th Conference of International Building Performance Simulation Association (Building Simulation 2013), Chambéry, France.

Freire, R. Z., Oliveira, G. H., & Mendes, N. (2008). Predictive controllers for thermal comfort optimization and energy savings. Energy and Buildings, 40(7), 1353–1365. https://doi.org/10.1016/j.enbuild.2007.12.007

Fux, S. F., Ashouri, A., Benz, M. J., & Guzzella, L. (2014). EKF based self-adaptive thermal model for a passive house. Energy and Buildings, 68, 811–817. https://doi.org/10.1016/j.enbuild.2012.06.016

Ganesh, H. S., Fritz, H. E., Edgar, T. F., Novoselac, A., & Baldea, M. (2019). A model-based dynamic optimization strategy for control of indoor air pollutants. Energy and Buildings, 195, 168–179. https://doi.org/10.1016/j.enbuild.2019.04.022

Ganesh, H. S., Seo, K., Fritz, H. E., Edgar, T. F., Novoselac, A., & Baldea, M. (2021). Indoor air quality and energy management in buildings using combined moving horizon estimation and model predictive control. Journal of Building Engineering, 33, 101552. https://doi.org/10.1016/j.jobe.2020.101552

Garfield, E. (1990). Keywords Plus®: ISI’s breakthrough retrieval method. Part 1. Expanding your searching power on Current Contents on Diskette. Current Contents®, 1(32), 5–9.

Garfield, E., & Sher, I. H. (1993). Keywords plus [TM]-algorithmic derivative indexing. Journal of the American Society for Information Science, 44, 298–298. 3.0.CO;2-A> https://doi.org/10.1002/(SICI)1097-4571(199306)44:5<298::AID-ASI5>3.0.CO;2-A

Godina, R., Rodrigues, E. M., Pouresmaeil, E., Matias, J. C., & Catalão, J. P. (2018). Model predictive control home energy management and optimization strategy with demand response. Applied Sciences, 8(3), 408. https://doi.org/10.3390/app8030408

Goyal, S., Ingley, H. A., & Barooah, P. (2012). Effect of various uncertainties on the performance of occupancy-based optimal control of HVAC zones. In 51st IEEE Conference on Decision and Control (CDC) (pp. 7565–7570). IEEE. https://doi.org/10.1109/CDC.2012.6426111

Hallinger, P., & Kovačević, J. (2019). A bibliometric review of research on educational administration: Science mapping the literature, 1960 to 2018. Review of Educational Research, 89(3), 335–369. https://doi.org/10.3102/0034654319830380

Hazyuk, I., Ghiaus, C., & Penhouet, D. (2012). Optimal temperature control of intermittently heated buildings using Model Predictive Control: Part I–Building modeling. Building and Environment, 51, 379–387. https://doi.org/10.1016/j.buildenv.2011.11.009

Henze, G. P., Dodier, R. H., & Krarti, M. (1997). Development of a predictive optimal controller for thermal energy storage systems. HVAC&R Research, 3(3), 233–264. https://doi.org/10.1080/10789669.1997.10391376

Hilliard, T., Kavgic, M., & Swan, L. (2016). Model predictive control for commercial buildings: trends and opportunities. Advances in Building Energy Research, 10(2), 172–190. https://doi.org/10.1080/17512549.2015.1079240

Hu, J., & Karava, P. (2014). Model predictive control strategies for buildings with mixed-mode cooling. Building and Environment, 71, 233–244. https://doi.org/10.1016/j.buildenv.2013.09.005

Hu, M., Xiao, F., Jørgensen, J. B., & Li, R. (2019). Price-responsive model predictive control of floor heating systems for demand response using building thermal mass. Applied Thermal Engineering, 153, 316–329. https://doi.org/10.1016/j.applthermaleng.2019.02.107

Huang, H., Chen, L., & Hu, E. (2015a). A neural network-based multi-zone modelling approach for predictive control system design in commercial buildings. Energy and Buildings, 97, 86–97. https://doi.org/10.1016/j.enbuild.2015.03.045

Huang, H., Chen, L., & Hu, E. (2015b). A new model predictive control scheme for energy and cost savings in commercial buildings: An airport terminal building case study. Building and Environment, 89, 203–216. https://doi.org/10.1016/j.buildenv.2015.01.037

Jazizadeh, F., Marin, F. M., & Becerik-Gerber, B. (2013). A thermal preference scale for personalized comfort profile identification via participatory sensing. Building and Environment, 68, 140–149. https://doi.org/10.1016/j.buildenv.2013.06.011

Jin, R., Zou, P. X., Piroozfar, P., Wood, H., Yang, Y., Yan, L., & Han, Y. (2019). A science mapping approach based review of construction safety research. Safety Science, 113, 285–297. https://doi.org/10.1016/j.ssci.2018.12.006

Kathirgamanathan, A., De Rosa, M., Mangina, E., & Finn, D. P. (2021). Data-driven predictive control for unlocking building energy flexibility: A review. Renewable and Sustainable Energy Reviews, 135, 110120. https://doi.org/10.1016/j.rser.2020.110120

Keim, D. A. (2002). Information visualization and visual data mining. IEEE Transactions on Visualization and Computer Graphics, 7(1), 100–107. https://doi.org/10.1109/2945.981847

Killian, M., & Kozek, M. (2016). Ten questions concerning model predictive control for energy efficient buildings. Building and Environment, 105, 403–412. https://doi.org/10.1016/j.buildenv.2016.05.034

Kim, H., Choi, H., Kang, H., An, J., Yeom, S., & Hong, T. (2021). A systematic review of the smart energy conservation system: From smart homes to sustainable smart cities. Renewable and Sustainable Energy Reviews, 140, 110755. https://doi.org/10.1016/j.rser.2021.110755

Kleinberg, J. (2003). Bursty and hierarchical structure in streams. Data Mining and Knowledge Discovery, 7(4), 373–397. https://doi.org/10.1023/A:1024940629314

Knudsen, M. D., & Petersen, S. (2016). Demand response potential of model predictive control of space heating based on price and carbon dioxide intensity signals. Energy and Buildings, 125, 196–204. https://doi.org/10.1016/j.enbuild.2016.04.053

Kolokotsa, D., Pouliezos, A., Stavrakakis, G., & Lazos, C. (2009). Predictive control techniques for energy and indoor environmental quality management in buildings. Building and Environment, 44(9), 1850–1863. https://doi.org/10.1016/j.buildenv.2008.12.007

Kusiak, A., Xu, G., & Zhang, Z. (2014). Minimization of energy consumption in HVAC systems with data-driven models and an interior-point method. Energy Conversion and Management, 85, 146–153. https://doi.org/10.1016/j.enconman.2014.05.053

Lee, K. H., & Braun, J. E. (2008). Model-based demand-limiting control of building thermal mass. Building and Environment, 43(10), 1633–1646. https://doi.org/10.1016/j.buildenv.2007.10.009

Li, L. L., Ding, G., Feng, N., Wang, M. H., & Ho, Y. S. (2009). Global stem cell research trend: Bibliometric analysis as a tool for mapping of trends from 1991 to 2006. Scientometrics, 80(1), 39–58. https://doi.org/10.1007/s11192-008-1939-5

Li, S., Joe, J., Hu, J., & Karava, P. (2015). System identification and model-predictive control of office buildings with integrated photovoltaic-thermal collectors, radiant floor heating and active thermal storage. Solar Energy, 113, 139–157. https://doi.org/10.1016/j.solener.2014.11.024

Li, X., & Malkawi, A. (2016). Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions. Energy, 112, 1194–1206. https://doi.org/10.1016/j.energy.2016.07.021

Ma, J., Qin, J., Salsbury, T., & Xu, P. (2012). Demand reduction in building energy systems based on economic model predictive control. Chemical Engineering Science, 67(1), 92–100. https://doi.org/10.1016/j.ces.2011.07.052

Ma, Y., Borrelli, F., Hencey, B., Coffey, B., Bengea, S., & Haves, P. (2011). Model predictive control for the operation of building cooling systems. IEEE Transactions on Control Systems Technology, 20(3), 796–803. https://doi.org/10.1109/TCST.2011.2124461

Maasoumy, M., Razmara, M., Shahbakhti, M., & Vincentelli, A. S. (2014). Handling model uncertainty in model predictive control for energy efficient buildings. Energy and Buildings, 77, 377–392. https://doi.org/10.1016/j.enbuild.2014.03.057

Manjarres, D., Mera, A., Perea, E., Lejarazu, A., & Gil-Lopez, S. (2017). An energy-efficient predictive control for HVAC systems applied to tertiary buildings based on regression techniques. Energy and Buildings, 152, 409–417. https://doi.org/10.1016/j.enbuild.2017.07.056

Mariano-Hernández, D., Hernández-Callejo, L., Zorita-Lamadrid, A., Duque-Pérez, O., & García, F. S. (2020). A review of strategies for building energy management system: Model predictive control, demand side management, optimization, and fault detect & diagnosis. Journal of Building Engineering, 33, 101692. https://doi.org/10.1016/j.jobe.2020.101692

Martínez, M. A., Cobo, M. J., Herrera, M., & Herrera-Viedma, E. (2015). Analyzing the scientific evolution of social work using science mapping. Research on Social Work Practice, 25(2), 257–277. https://doi.org/10.1177/1049731514522101

May-Ostendorp, P., Henze, G. P., Corbin, C. D., Rajagopalan, B., & Felsmann, C. (2011). Model-predictive control of mixed-mode buildings with rule extraction. Building and Environment, 46(2), 428–437. https://doi.org/10.1016/j.buildenv.2010.08.004

Megahed, T. F., Abdelkader, S. M., & Zakaria, A. (2019). Energy management in zero-energy building using neural network predictive control. IEEE Internet of Things Journal, 6(3), 5336–5344. https://doi.org/10.1109/JIOT.2019.2900558

Menon, R. P., Maréchal, F., & Paolone, M. (2016). Intra-day electro-thermal model predictive control for polygeneration systems in microgrids. Energy, 104, 308–319. https://doi.org/10.1016/j.energy.2016.03.081

Merabet, G. H., Essaaidi, M., Haddou, M. B., Qolomany, B., Qadir, J., Anan, M., Al-Fuqaha, A., Abid, M. R., & Benhaddou, D. (2021). Intelligent building control systems for thermal comfort and energy-efficiency: A systematic review of artificial intelligence-assisted techniques. Renewable and Sustainable Energy Reviews, 144, 110969. https://doi.org/10.1016/j.rser.2021.110969

Mirakhorli, A., & Dong, B. (2016). Occupancy behavior based model predictive control for building indoor climate – A cri­tical review. Energy and Buildings, 129, 499–513. https://doi.org/10.1016/j.enbuild.2016.07.036

Moroşan, P. D., Bourdais, R., Dumur, D., & Buisson, J. (2010). Building temperature regulation using a distributed model predictive control. Energy and Buildings, 42(9), 1445–1452. https://doi.org/10.1016/j.enbuild.2010.03.014

Morris, S. A., Yen, G., Wu, Z., & Asnake, B. (2003). Time line visualization of research fronts. Journal of the American Society for Information Science and Technology, 54(5), 413–422. https://doi.org/10.1002/asi.10227

Mossolly, M., Ghali, K., & Ghaddar, N. (2009). Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm. Energy, 34(1), 58–66. https://doi.org/10.1016/j.energy.2008.10.001

Mtibaa, F., Nguyen, K. K., Dermardiros, V., & Cheriet, M. (2021). Context-aware Model Predictive Control framework for multi-zone buildings. Journal of Building Engineering, 42, 102340. https://doi.org/10.1016/j.jobe.2021.102340

Naji, N., Abid, M. R., Krami, N., & Benhaddou, D. (2019). An energy-aware wireless sensor network for data acquisition in smart energy efficient building. In 2019 IEEE 5th World Forum on Internet of Things (WF-IoT) (pp. 7–12). IEEE. https://doi.org/10.1109/WF-IoT.2019.8767308

Oldewurtel, F., Parisio, A., Jones, C. N., Gyalistras, D., Gwerder, M., Stauch, V., Lehmann, B., & Morari, M. (2012). Use of model predictive control and weather forecasts for energy efficient building climate control. Energy and Buildings, 45, 15–27. https://doi.org/10.1016/j.enbuild.2011.09.022

Pazheri, F. R., Othman, M. F., & Malik, N. H. (2014). A review on global renewable electricity scenario. Renewable and Sustainable Energy Reviews, 31, 835–845. https://doi.org/10.1016/j.rser.2013.12.020

Peng, Y., Rysanek, A., Nagy, Z., & Schlüter, A. (2017). Occupancy learning-based demand-driven cooling control for office spaces. Building and Environment, 122, 145–160. https://doi.org/10.1016/j.buildenv.2017.06.010

Pérez-Lombard, L., Ortiz, J., & Pout, C. (2008). A review on buildings energy consumption information. Energy and Buildings, 40(3), 394–398. https://doi.org/10.1016/j.enbuild.2007.03.007

Picard, D., Drgoňa, J., Kvasnica, M., & Helsen, L. (2017). Impact of the controller model complexity on model predictive control performance for buildings. Energy and Buildings, 152, 739–751. https://doi.org/10.1016/j.enbuild.2017.07.027

Privara, S., Široký, J., Ferkl, L., & Cigler, J. (2011). Model predictive control of a building heating system: The first experience. Energy and Buildings, 43(2–3), 564–572. https://doi.org/10.1016/j.enbuild.2010.10.022

Rahmani-Andebili, M. (2017). Scheduling deferrable appliances and energy resources of a smart home applying multi-time scale stochastic model predictive control. Sustainable Cities and Society, 32, 338–347. https://doi.org/10.1016/j.scs.2017.04.006

Reynolds, J., Rezgui, Y., Kwan, A., & Piriou, S. (2018). A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control. Energy, 151, 729–739. https://doi.org/10.1016/j.energy.2018.03.113

Ríos-Moreno, G. J., Trejo-Perea, M., Castañeda-Miranda, R., Hernández-Guzmán, V. M., & Herrera-Ruiz, G. (2007). Modelling temperature in intelligent buildings by means of autoregressive models. Automation in Construction, 16(5), 713–722. https://doi.org/10.1016/j.autcon.2006.11.003

Robillart, M., Schalbart, P., Chaplais, F., & Peuportier, B. (2019). Model reduction and model predictive control of energy-efficient buildings for electrical heating load shifting. Journal of Process Control, 74, 23–34. https://doi.org/10.1016/j.jprocont.2018.03.007

Rockett, P., & Hathway, E. A. (2017). Model-predictive control for non-domestic buildings: a critical review and prospects. Building Research & Information, 45(5), 556–571. https://doi.org/10.1080/09613218.2016.1139885

Ruusu, R., Cao, S., Delgado, B. M., & Hasan, A. (2019). Direct quantification of multiple-source energy flexibility in a residential building using a new model predictive high-level controller. Energy Conversion and Management, 180, 1109–1128. https://doi.org/10.1016/j.enconman.2018.11.026

Salakij, S., Yu, N., Paolucci, S., & Antsaklis, P. (2016). Model-Based Predictive Control for building energy management. I: Energy modeling and optimal control. Energy and Buildings, 133, 345–358. https://doi.org/10.1016/j.enbuild.2016.09.044

Schirrer, A., Brandstetter, M., Leobner, I., Hauer, S., & Kozek, M. (2016). Nonlinear model predictive control for a heating and cooling system of a low-energy office building. Energy and Buildings, 125, 86–98. https://doi.org/10.1016/j.enbuild.2016.04.029

Schneider, J., & Kirkpatrick, S. (2007). Stochastic optimization. Springer Science & Business Media.

Sepasgozar, S., Karimi, R., Farahzadi, L., Moezzi, F., Shirowzhan, S., Ebrahimzadeh, S. M., Hui, F., & Aye, L. (2020). A systematic content review of artificial intelligence and the internet of things applications in smart home. Applied Sciences, 10(9), 3074. https://doi.org/10.3390/app10093074

Serale, G., Fiorentini, M., Capozzoli, A., Bernardini, D., & Bemporad, A. (2018). Model predictive control (MPC) for enhancing building and HVAC system energy efficiency: Problem formulation, applications and opportunities. Energies, 11(3), 631. https://doi.org/10.3390/en11030631

Shakeri, M., Shayestegan, M., Abunima, H., Reza, S. S., Akhtaruzzaman, M., Alamoud, A. R. M., Sopian, K., & Amin, N. (2017). An intelligent system architecture in home energy management systems (HEMS) for efficient demand response in smart grid. Energy and Buildings, 138, 154–164. https://doi.org/10.1016/j.enbuild.2016.12.026

Široký, J., Oldewurtel, F., Cigler, J., & Prívara, S. (2011). Experimental analysis of model predictive control for an energy efficient building heating system. Applied Energy, 88(9), 3079–3087. https://doi.org/10.1016/j.apenergy.2011.03.009

Sultana, W. R., Sahoo, S. K., Sukchai, S., Yamuna, S., & Venkatesh, D. (2017). A review on state of art development of model predictive control for renewable energy applications. Renewable and Sustainable Energy Reviews, 76, 391–406. https://doi.org/10.1016/j.rser.2017.03.058

Sun, W., & Yuan, Y. X. (2006). Optimization theory and methods: nonlinear programming (Vol. 1). Springer Science & Business Media.

Sun, Y., Haghighat, F., & Fung, B. C. (2020). A review of the-state-of-the-art in data-driven approaches for building energy prediction. Energy and Buildings, 221, 110022. https://doi.org/10.1016/j.enbuild.2020.110022

Thomas, D., Deblecker, O., & Ioakimidis, C. S. (2018). Optimal operation of an energy management system for a grid-connected smart building considering photovoltaics’ uncertainty and stochastic electric vehicles’ driving schedule. Applied Energy, 210, 1188–1206. https://doi.org/10.1016/j.apenergy.2017.07.035

Toub, M., Reddy, C. R., Razmara, M., Shahbakhti, M., Robinett III, R. D., & Aniba, G. (2019). Model-based predictive control for optimal MicroCSP operation integrated with building HVAC systems. Energy Conversion and Management, 199, 111924. https://doi.org/10.1016/j.enconman.2019.111924

US Environmental Protection Agency (EPA). (1989). Report to Congress on indoor air quality, volume II: assessment and control of indoor air pollution (Technical Report EPA/400/1-89/001C).

Van Eck, N. J., & Waltman, L. (2010). Software survey: VOSviewer, a computer program for bibliometric mapping. Scientometrics, 84(2), 523–538. https://doi.org/10.1007/s11192-009-0146-3

Van Eck, N. J., & Waltman, L. (2014). Visualizing bibliometric networks. In Y. Ding, R. Rousseau, & D. Wolfram (Eds.), Measuring scholarly impact (pp. 285–320). Springer, Cham. https://doi.org/10.1007/978-3-319-10377-8_13

Van Eck, N. J., & Waltman, L. (2020). VOSviewer manual. Manual for VOSviewer version 1.6.16. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.16.pdf

Vedullapalli, D. T., Hadidi, R., & Schroeder, B. (2019). Optimal demand response in a building by battery and HVAC scheduling using model predictive control. In 2019 IEEE/IAS 55th Industrial and Commercial Power Systems Technical Conference (I&CPS). IEEE. https://doi.org/10.1109/ICPS.2019.8733344

Vose, M. D. (1999). The simple genetic algorithm: Foundations and theory. MIT Press.

Waltman, L., & Van Eck, N. J. (2013). A smart local moving algorithm for large-scale modularity-based community detection. The European Physical Journal B, 86(11), 471. https://doi.org/10.1140/epjb/e2013-40829-0

Waltman, L., Van Eck, N. J., & Noyons, E. C. (2010). A unified approach to mapping and clustering of bibliometric networks. Journal of Informetrics, 4(4), 629–635. https://doi.org/10.1016/j.joi.2010.07.002

Wang, S., & Jin, X. (2000). Model-based optimal control of VAV air-conditioning system using genetic algorithm. Building and Environment, 35(6), 471–487. https://doi.org/10.1016/S0360-1323(99)00032-3

Wang, Z., Wang, L., Dounis, A. I., & Yang, R. (2012). Multi-agent control system with information fusion based comfort model for smart buildings. Applied Energy, 99, 247–254. https://doi.org/10.1016/j.apenergy.2012.05.020

Wanjiru, E. M., Sichilalu, S. M., & Xia, X. (2017). Model predictive control of heat pump water heater-instantaneous shower powered with integrated renewable-grid energy systems. Applied Energy, 204, 1333–1346. https://doi.org/10.1016/j.apenergy.2017.05.033

West, S. R., Ward, J. K., & Wall, J. (2014). Trial results from a model predictive control and optimisation system for commercial building HVAC. Energy and Buildings, 72, 271–279. https://doi.org/10.1016/j.enbuild.2013.12.037

Xi, X. C., Poo, A. N., & Chou, S. K. (2007). Support vector regression model predictive control on a HVAC plant. Control Engineering Practice, 15(8), 897–908. https://doi.org/10.1016/j.conengprac.2006.10.010

Yang, S., Wan, M. P., Ng, B. F., Zhang, T., Babu, S., Zhang, Z., Chen, W., & Dubey, S. (2018). A state-space thermal model incorporating humidity and thermal comfort for model predictive control in buildings. Energy and Buildings, 170, 25–39. https://doi.org/10.1016/j.enbuild.2018.03.082

Yang, S., Wan, M. P., Chen, W., Ng, B. F., & Dubey, S. (2020). Model predictive control with adaptive machine-learning-based model for building energy efficiency and comfort optimization. Applied Energy, 271, 115147. https://doi.org/10.1016/j.apenergy.2020.115147

Zhan, S., & Chong, A. (2021). Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective. Renewable and Sustainable Energy Reviews, 142, 110835. https://doi.org/10.1016/j.rser.2021.110835

Zhang, J., Yu, Q., Zheng, F., Long, C., Lu, Z., & Duan, Z. (2016). Comparing keywords plus of WOS and author keywords: A case study of patient adherence research. Journal of the Association for Information Science and Technology, 67(4), 967–972. https://doi.org/10.1002/asi.23437

Zhu, J., Shen, Y., Song, Z., Zhou, D., Zhang, Z., & Kusiak, A. (2019). Data-driven building load profiling and energy management. Sustainable Cities and Society, 49, 101587. https://doi.org/10.1016/j.scs.2019.101587

Zhuang, J., Chen, Y., & Chen, X. (2018). A new simplified modeling method for model predictive control in a medium-sized commercial building: A case study. Building and Environment, 127, 1–12. https://doi.org/10.1016/j.buildenv.2017.10.022