Architecting sustainability performances and enablers for grid-interactive efficient buildings

Riadh Habash; Md Mahmud  Hasan

doi:10.59400/be.v2i1.1301

Architecting sustainability performances and enablers for grid-interactive efficient buildings

Riadh Habash School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada
Md Mahmud Hasan School of Electrical Engineering and Computer Science, University of Ottawa, Ottawa, ON K1N 6N5, Canada

Ariticle ID: 1301

59 Views, 31 PDF Downloads

DOI: https://doi.org/10.59400/be.v2i1.1301

Keywords: grid-interactive efficient building; digital twins; building information modeling; human-in-the-loop; human-cyber-physical security

Abstract

Today, grid-interactive efficient buildings are gaining popularity due to their potential sustainability performances through their ability to learn, adapt, and evolve at different scales to improve the quality of life of their users while optimizing resource usage and service availability. This is realized through various practices such as management and control measures enabled by smart grid technologies, interoperability, and human-cyber-physical security. However, despite their great potential, the research of those technologies still faces various challenges. These include a lack of communication and control infrastructure to address interpretability, security, cost barriers, and difficulties balancing occupant needs with grid benefits. Initially, system modelling and simulation are promising approaches to address those challenges ahead of time. It involves the consideration of complex systems made up of components from various research domains. This paper addresses the above practices, highlighting the value of integrating technology and intelligence in the planning and operation of buildings, both new and old. It provides a way to educate architects and engineers about this emerging field and demonstrates how these practices can help in creating efficient, resilient, and secure buildings that contribute to occupant comfort and decarbonization.

References

[1] NIST. Commissioning building systems for improved energy performance. Available online: https://www.nist.gov/programs-projects/commissioning-building-systems-improved-energy-performance (accessed on 16 January 2024).

[2] Aguilar J, Garces-Jimenez A, R-Moreno MD, et al. A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings. Renewable and Sustainable Energy Reviews. 2021; 151: 111530. doi: 10.1016/j.rser.2021.111530

[3] Neukomm M, Nubbe V, Fares R. Grid-interactive efficient buildings. Available online: https://www1.eere.energy.gov/buildings/pdfs/75470.pdf (accessed on 22 January 2024).

[4] Habash R. Sustainability and health of intelligent buildings, 1st ed. Woodhead Publishing; 2022.

[5] Peterson K, Torcellini P, Grant R, et al. A common definition for zero energy buildings. Available online: https://www.energy.gov/sites/prod/files/2015/09/f26/bto_common_definition_zero_energy_buildings_093015.pdf (accessed on 4 January 2024).

[6] ASHRAE. ASHRAE releases guide on the role of grid interactivity in decarbonization. Available online: https://www.ashrae.org/about/news/2023/ashrae-releases-guide-on-the-role-of-grid-interactivity-in-decarbonization (accessed on 13 January 2024).

[7] Hussain I, Ullah M, Ullah I, et al. Optimizing energy consumption in the home energy management system via a bio-inspired dragonfly algorithm and the genetic algorithm. Electronics. 2020; 9(3): 406. doi: 10.3390/electronics9030406

[8] Kolokotsa D. The role of smart grids in the building sector. Energy and Buildings. 2016; 116: 703-708. doi: 10.1016/j.enbuild.2015.12.033

[9] Rahman S, Haque A, Jing Z. Modeling and performance evaluation of grid-interactive efficient buildings (GEB) in a microgrid environment. IEEE Open Access Journal of Power and Energy. 2021; 8: 423-432. doi: 10.1109/oajpe.2021.3098660

[10] Chang S, Yang PPJ, Yamagata Y, et al. Modeling and design of smart buildings. Urban Systems Design. Published online 2020: 59-86. doi: 10.1016/b978-0-12-816055-8.00003-8

[11] Chang S, Castro-Lacouture D, Matsui K, et al. Planning and monitoring of building energy demands under uncertainties by using IoT data. Computing in Civil Engineering 2019. Published online June 13, 2019. doi: 10.1061/9780784482445.027

[12] Wang Z, Srinivasan RS. A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews. 2017; 75: 796-808. doi: 10.1016/j.rser.2016.10.079

[13] Crawley DB, Lawrie LK, Winkelmann FC, et al. EnergyPlus: Creating a new-generation building energy simulation program. Energy Building. 2001; 33(4): 319-331.

[14] Qureshi FA, Lymperopoulos I, Khatir AA, et al. Economic advantages of office buildings providing ancillary services with intraday participation. IEEE Transactions on Smart Grid. 2018; 9(4): 3443-3452. doi: 10.1109/tsg.2016.2632239

[15] Huang S, Wu D. Validation on aggregate flexibility from residential air conditioning systems for building-to-grid integration. Energy and Buildings. 2019; 200: 58-67. doi: 10.1016/j.enbuild.2019.07.043

[16] Liu M, Heiselberg P. Energy flexibility of a nearly zero-energy building with weather predictive control on a convective building energy system and evaluated with different metrics. Applied Energy. 2019; 233-234: 764-775. doi: 10.1016/j.apenergy.2018.10.070

[17] Li X, Malkawi A. Multi-objective optimization for thermal mass model predictive control in small and medium size commercial buildings under summer weather conditions. Energy. 2016; 112: 1194-1206. doi: 10.1016/j.energy.2016.07.021

[18] Taha AF, Gatsis N, Dong B, et al. Buildings-to-grid integration framework. IEEE Transactions on Smart Grid. 2019; 10(2): 1237-1249. doi: 10.1109/tsg.2017.2761861

[19] Joe J, Karava P. A model predictive control strategy to optimize the performance of radiant floor heating and cooling systems in office buildings. Applied Energy. 2019; 245: 65-77. doi: 10.1016/j.apenergy.2019.03.209

[20] Jiang T, Ju P, Wang C, et al. Coordinated control of air-conditioning loads for systemfrequency regulation. IEEE Transactions on Smart Grid. 2021; 12(1): 548-560. doi: 10.1109/tsg.2020.3022010

[21] Mai W, Chung CY. Economic MPC of aggregating commercial buildings for providing flexible power reserve. IEEE Transactions on Power Systems. 2015; 30(5): 2685-2694. doi: 10.1109/tpwrs.2014.2365615

[22] Baranski M, Meyer L, Fütterer J, et al. Comparative study of neighbor communication approaches for distributed model predictive control in building energy systems. Energy. 2019; 182: 840-851. doi: 10.1016/j.energy.2019.06.037

[23] Chellaswamy C, Ganesh Babu R, Vanathi A. A framework for building energy management system with residence mounted photovoltaic. Building Simulation. 2021; 14(4): 1031-1046. doi: 10.1007/s12273-020-0735-x

[24] Becerik-Gerber B, Lucas G, Aryal A, et al. The field of human building interaction for convergent research and innovation for intelligent built environments. Scientific Reports. 2022; 12(1). doi: 10.1038/s41598-022-25047-y

[25] ANSI/ASHRAE. Thermal environmental conditions for human occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE 55:2020); 2020.

[26] ISO. Analytical determination and interpretation of thermal comfort using calculation of the PMV and PPD indices and local thermal comfort criteria—Ergonomics of the thermal environment (ISO 7730:2005). International Organization for Standardization; 2005.

[27] CEN. Energy performance of buildings—Ventilation for buildings—Part 1: Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics—Module Ml-6, European Committee for Standardization (EN 16798–1:2019). Available online: https://www.sis.se/en/produkter/construction-materials-and-building/installations-in-buildings/general/ss-en-16798-12019/ (accessed on 29 November 2023).

[28] CEN. Indoor environmental input parameters for design and assessment of energy performance of buildings addressing indoor air quality, thermal environment, lighting and acoustics (EN 15251:2007). Available online: https://standards.iteh.ai/catalog/standards/cen/92485123-bf64-40e3-9387-9724a642eae8/en-15251-2007 (accessed on 7 November 2023).

[29] Fanger PO. Thermal comfort: analysis and applications in environmental engineering. Danish Technical Press; 1970.

[30] Segovia E, van Schaik P, Vukovic V. Indoor thermal comfort controller integrating human interaction in the control-loop as a live component. In: Nixon JD, Al-Habaibeh A, Vukovic V, et al. Energy and Sustainable Futures: Proceedings of the 3rd ICESF, 2022. Springer Nature Switzerland; 2023. doi: 10.1007/978-3-031-30960-1

[31] Volk R, Stengel J, Schultmann F. Building Information Modeling (BIM) for existing buildings—Literature review and future needs. Automation in Construction. 2014; 38: 109-127. doi: 10.1016/j.autcon.2013.10.023

[32] Bortolini R, Rodrigues R, Alavi H, et al. Digital twins’ applications for building energy efficiency: A Review. Energies. 2022; 15(19): 7002. doi: 10.3390/en15197002

[33] Hou H, Lai JHK, Wu H, et al. Digital twin application in heritage facilities management: Systematic literature review and future development directions. Engineering, Construction and Architectural Management. Published online March 28, 2023. doi: 10.1108/ecam-06-2022-0596

[34] Thebault M, Gaillard L. Optimization of the integration of photovoltaic systems on buildings for self-consumption – Case study in France. City and Environment Interactions. 2021; 10: 100057. doi: 10.1016/j.cacint.2021.100057

[35] Fath K, Stengel J, Sprenger W, et al. A method for predicting the economic potential of (building-integrated) photovoltaics in urban areas based on hourly radiance simulations. Solar Energy. 2015; 116: 357-370. doi: 10.1016/j.solener.2015.03.023

[36] Redweik P, Catita C, Brito M. Solar energy potential on roofs and facades in an urban landscape. Solar Energy. 2013; 97: 332-341. doi: 10.1016/j.solener.2013.08.036

[37] Allouhi A, Saadani R, Kousksou T, et al. Grid-connected PV systems installed on institutional buildings: Technology comparison, energy analysis and economic performance. Energy and Buildings. 2016; 130: 188-201. doi: 10.1016/j.enbuild.2016.08.054

[38] Sahri Y, Tamalouzt S, Belaid SL, et al. Performance improvement of Hybrid System based DFIG-Wind/PV/Batteries connected to DC and AC grid by applying Intelligent Control. Energy Reports. 2023; 9: 2027-2043. doi: 10.1016/j.egyr.2023.01.021

[39] Khosravi N, Baghbanzadeh R, Oubelaid A, et al. A novel control approach to improve the stability of hybrid AC/DC microgrids. Applied Energy. 2023; 344: 121261. doi: 10.1016/j.apenergy.2023.121261

[40] Zhou J, Zhou Y, Wang B, et al. Human–cyber–physical systems (HCPSs) in the context of new-generation intelligent manufacturing. Engineering. 2019; 5(4): 624-636. doi: 10.1016/j.eng.2019.07.015

[41] Wang B, Zheng P, Yin Y, et al. Toward human-centric smart manufacturing: A human-cyber-physical systems (HCPS) perspective. Journal of Manufacturing Systems. 2022; 63: 471-490. doi: 10.1016/j.jmsy.2022.05.005

[42] Schoechle T. Re-inventing wires: the future of landlines and networks, 2018. Available online: https://electromagnetichealth.org/wp-content/uploads/2018/02/ReInventing-Wires-1-25-18.pdf (accessed on 8 January 2024).

[43] Wu H, Han H, Wang X, et al. Research on artificial intelligence enhancing Internet of Things security: A survey. IEEE Access. 2020; 8: 153826-153848. doi: 10.1109/access.2020.3018170

[44] O’Brien L. Cybersecurity for smart buildings, 2019. Available online: https://www.arcweb.com/blog/cybersecurity-smart-buildings (accessed on 9 January 2024).

[45] Hasan MM, Mouftah HT. Cyber‐physical vulnerabilities of wireless sensor networks in smart cities. Security and Privacy in Cyber‐Physical Systems. Published online October 6, 2017: 263-280. doi: 10.1002/9781119226079.ch13

[46] Hasan MM, Mouftah HT. Cloud-centric collaborative security service placement for advanced metering infrastructures. IEEE Transactions on Smart Grid. 2019; 10(2): 1339-1348. doi: 10.1109/tsg.2017.2763954

[47] Hasan MM, Mouftah HT. Encryption as a service for smart grid advanced metering infrastructure. 2015 IEEE Symposium on Computers and Communication (ISCC). Published online July 2015. doi: 10.1109/iscc.2015.7405519

[48] Aikin KE. The future of grid-interactive efficient buildings and local transactive energy markets. The future of decentralized electricity distribution networks. Published online 2023: 437-463. doi: 10.1016/b978-0-443-15591-8.00022-x

Published

2024-05-31

How to Cite

Habash, R., & Hasan, M. M. (2024). Architecting sustainability performances and enablers for grid-interactive efficient buildings. Building Engineering, 2(1), 1301. https://doi.org/10.59400/be.v2i1.1301

Download Citation

Issue

Vol. 2 No. 1 (2024)

Section

Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it for any purpose, even commercially, under the condition that the authors are given credit. With this license, authors hold the copyright.

Editor-in-Chief

Prof. Scholz Miklas
University of Johannesburg, South Africa

eISSN

3029-2670

Publication Frequency

Bi-annual

About the Publisher

Academic Publishing insists on taking academic exchange and publication as the main line, carrying out comprehensive management based on science and technology, and fully exploring excellent international publishing resources. Within 5 years, it will form a strategic framework and scale with science (S), technology (T), medicine (M), education (E), and humanities and arts (H) as the main publishing fields. Academic Publishing is headquartered in Singapore and based in Malaysia, with the United States and China providing the main scientific and academic resources. At the same time, it has established long-term good cooperative relations with other publishing companies, scientific research communities, and academic organizations in more than a dozen countries and regions. Academic Publishing uses English and Chinese as its main publishing languages, mainly publishing books, journals, and conference papers in print and online. The vast majority of publications follow the international open access policy, providing stable and long-term quality and professional publications. With the joint efforts of the expert team and our professional editorial team, our publications will gradually be indexed by international databases in stages to provide convenient and professional retrieval for various scholars. At the same time, manuscripts we accept will be subject to the peer review principle, and cutting-edge and innovative research articles will be preferentially accepted for peer reference and discussion. All kinds of our publications are welcome for peer to contribute, access, and download.

more

Volume Arrangement

2024

Vol 2, No 1 (2024)

2023

Vol 1, No 1 (2023)

Featured Articles

Comprehensive seismic loss model of Tehran, Iran in the case of Mosha fault seismic scenario using stochastic finite-fault method

This paper presents the results of a study caried out to assess probable seismic loss, in term of damage to the residential buildings and the number of fatalities, in the case of Mosha Fault seismic scenario in Tehran, Iran. Accordingly, seismic risk components (including seismic hazard, exposure model and fragility curves) are evaluated. The stochastic finite-fault method with dynamic corner frequency is applied for quantifying ground motion values. The results shows that PGA on the soil surface could range between 0.1 g to 0.45 g. Then, a reliable model of building exposure by analyzing census data from Tehran is compiled. This model included 19 different classes of buildings and is used to evaluate the potential damage to buildings from seismic scenario. The results indicate that the median of damage ratio from 100,000 iterations for the whole of the city is about 6% ± 1.54%. The study found that the central and eastern parts of Tehran are the most vulnerable areas, with an estimated 15,952 residents at risk of losing their lives in this scenario. This is equivalent to 0.2 percent of total population of Tehran. The finding from this study can be used by local authorities to provide appropriate emergency-response and preparedness plans in the case of Mosha Fault seismic scenario.

Considerations for the effective management of LANs in hospital buildings

According to a joint survey conducted by the Ministry of Internal Affairs and Communications and the Ministry of Health, Labour, and Welfare of Japan, over 90% of Japanese hospitals have introduced wireless LAN. However, about half of the hospitals that have wireless LANs have reported having experienced problems, with the most common cause being inappropriate management of signal propagation. Other factors include an excessive number of terminals connected to one AP, an information load that approaches or exceeds the limits of the network’s capacity, and a lack of information sharing during design and construction. There is also a move towards converting in-hospital PHS, which has been used mainly for nurse calls, to smartphones, which also provide voice communication over a wireless LAN. Furthermore, the number of medical devices with wireless LAN communication functions is increasing. In recent years, issues have emerged regarding the coexistence of wireless LANs used by patients. This includes security aspects, as with problematic operation of wireless LANs. The above could become even more significant considerations in future wireless LAN utilization. In this paper, we summarize issues we have identified, clarify their causes, and present possible future problems and current and future measures to be taken for their solution.

Digital transformation of quality management in the construction industry during the execution phase by integration of building information modeling (BIM) and cloud computing

The quality of construction projects significantly impacts social and economic development. However, low quality and project failure often result from factors such as lack of quality procedures, poor communication, task coordination, and inefficient progress monitoring. This research aims to improve the efficiency of the construction phase by creating quality control checklists for processes and enhancing quality management through a collaborative digital environment integrating building information modeling (BIM) and cloud computing. Expert constructive interviews were first conducted to define a construction process quality control procedure to be linked to the 3DBIM model and then transition to a collaborative cloud environment (Autodesk Construction Cloud). An actual instance in Latakia City (Syria) demonstrated that the proposed methodology improves the efficiency and effectiveness of quality management during the implementation phase. It does so by offering a robust database, enhancing on-site quality information extraction from BIM models using smartphones, documenting defects and entering inspection data directly into a shared digital environment, and making it easier to track corrective actions and feedback. This facilitates constant and organized access to current data, reducing errors and rework, saving money and time, and enhancing decision-making speed and effectiveness. The search recommends the necessity of strict laws to adhere to quality procedures and the importance of providing infrastructure for digital transformation in quality management.

Net zero energy analysis and energy conversion of sustainable residential building in Muscat, Oman

The building sector is the predominant consumer of primary energy globally. The building sector accounts for around 40% of global energy production.Net Zero Energy Buildings (NZEBs) are highly suggested by energy experts as an effective option to alleviate the strain on primary energy sources caused by the building sector. The disparity between energy performance predictions provided during the design phase and the actual energy performance of residential buildings is mostly attributed to a limited comprehension of the components that influence energy consumption and the constraints of whole building simulation software. The objective of this research was to perform a comparison analysis of the expected and actual energy consumption of a prototype net-zero energy house built at the University of Technology and Applied Sciences in Muscat. The Hourly Analysis Programme (HAP V4.2) was utilised to forecast the energy consumption of a Net Zero Energy Building (NZEB) at HCT, taking into account the availability of an Energy Recovery Ventilator (ERV) and the absence of an ERV. The newly built house underwent a one-month testing phase to fulfil many duties according to competition regulations. One of the main goals was to generate on-site energy through photovoltaic panels, producing an amount proportional to the energy consumed by the house. Upon comparing the actual energy consumption data with the simulated result, it was noticed that the actual energy demand of the house was around 20% lower than the prediction made by the simulation tool.

Architecting sustainability performances and enablers for grid-interactive efficient buildings