Review and Decision-Making Tree for Methods to Balance Indoor Environmental Comfort and Energy Conservation During Building Operation
Abstract
1. Introduction
2. Research Methodology
2.1. Significance of Energy Conservation and Indoor Environmental Comfort During Building Operation
2.2. Literature Review Design
3. Main Components and Key Comfort Indicators
3.1. Main Energy-Consuming Components During Building Operation
3.1.1. Building Envelope
3.1.2. HVAC Systems
3.1.3. Lighting Systems
3.1.4. Plug Loads and Appliances
3.2. Key Indicators of Indoor Environmental Comfort
3.2.1. Thermal Comfort Indicators
3.2.2. Lighting Comfort Indicators
3.2.3. Indoor Air Quality Comfort Indicators
4. Energy Conservation Methods Considering Indoor Environmental Comfort
4.1. Passive Strategies
4.2. Control Optimization Strategies
4.3. Behavioural Intervention Strategies
5. Discussion
5.1. Status Quo of Operational Energy Conversation Practices
5.1.1. Interdependencies Between Main Components and Key Comfort Indicators
5.1.2. Classification and Evaluation Criteria of Methods
5.2. From the Literature to Application: A Decision-Making Tree Based on Survey Results
5.2.1. Key Survey Insights
5.2.2. Decision-Making Tree Generation
6. Conclusions
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Appendix A
Survey on Influencing Factors for Selecting Energy Conservation Methods in Building Operation
References
- Fang, Z.; Yan, J.; Lu, Q.; Chen, L.; Yang, P.; Tang, J.; Jiang, F.; Broyd, T.; Hong, J. A Systematic Literature Review of Carbon Footprint Decision-Making Approaches for Infrastructure and Building Projects. Appl. Energy 2023, 335, 120768. [Google Scholar] [CrossRef]
- The International Energy Agency. CO2 Emissions in 2022; IEA: Paris, France, 2023. [Google Scholar]
- Bui, T.; Domingo, N.; MacGregor, C.; Wilkinson, S. Zero Carbon Refurbishment for Existing Buildings: A Literature Review. In Proceedings of the IOP Conference Series: Earth and Environmental Science, Melbourne, Australia, 26–30 June 2022; IOP Publishing: Bristol, UK, 2022; Volume 1101, p. 022017. [Google Scholar]
- Yang, X.; Gu, Z.; Yang, J.; Lin, G. Review on the Research of Indoor Environment Quality and Building Energy Consumption. Appl. Mech. Mater. 2011, 90–93, 3043–3046. [Google Scholar] [CrossRef]
- Xie, J.; Li, H.; Li, C.; Zhang, J.; Luo, M. Review on Occupant-Centric Thermal Comfort Sensing, Predicting, and Controlling. Energy Build. 2020, 226, 110392. [Google Scholar] [CrossRef]
- Use of Energy in Commercial Buildings—U.S. Energy Information Administration (EIA). Available online: https://www.eia.gov/energyexplained/use-of-energy/commercial-buildings.php (accessed on 15 May 2024).
- Kiviste, M.; Musakka, S.; Ruus, A.; Vinha, J. A Review of Non-Residential Building Renovation and Improvement of Energy Efficiency: Office Buildings in Finland, Sweden, Norway, Denmark, and Germany. Energies 2023, 16, 4220. [Google Scholar] [CrossRef]
- Patrício, J.T.; Lopes, R.A.; Majdalani, N.; Aelenei, D.; Martins, J. Aggregated Use of Energy Flexibility in Office Buildings. Energies 2023, 16, 961. [Google Scholar] [CrossRef]
- Raw, G.J.; Littleford, C.; Clery, L. Saving Energy with a Better Indoor Environment. Archit. Sci. Rev. 2017, 60, 239–248. [Google Scholar] [CrossRef]
- Ribeiro, C.F.T.; Ramos, N.M.M.; Flores-Colen, I. Spaces In-between Impacts on Indoor Environment and Energy Efficiency in Dwellings. In Proceedings of the MATEC Web of Conferences, Prague, Czechia, 2 September 2019; Cerny, R., Koci, J., Koci, V., Eds.; EDP Sciences: Les Ulis, France, 2019; Volume 282, p. 02071. [Google Scholar]
- Yuan, F.; Yao, R.; Sadrizadeh, S.; Li, B.; Cao, G.; Zhang, S.; Zhou, S.; Liu, H.; Bogdan, A.; Croitoru, C.; et al. Thermal Comfort in Hospital Buildings—A Literature Review. J. Build. Eng. 2022, 45, 103463. [Google Scholar] [CrossRef]
- Niza, I.L.; Cordeiro Gomes, G.C.; Broday, E.E. Indoor Environmental Quality Models: A Bibliometric, Mapping and Clustering Review. Renew. Sustain. Energy Rev. 2024, 203, 114791. [Google Scholar] [CrossRef]
- Amasyali, K.; El-Gohary, N. Building Energy Use Modes and Thermal Comfort. In Proceedings of the Computing in Civil Engineering 2017: Information Modelling and Data Analytics, Seattle, WA, USA, 25–27 June 2017; Lin, K.Y., ElGohary, N., Tang, P., Eds.; American Society of Civil Engineers: New York, NY, USA, 2017; pp. 350–358. [Google Scholar]
- Gatea, A.A.; Batcha, M.F.M.; Taweekun, J. Energy Efficiency and Thermal Comfort in Hospital Buildings: A Review. Int. J. Integr. Eng. 2020, 12, 33–41. [Google Scholar] [CrossRef]
- Matsui, K.; Saito, K. IoT-Based OLED Lighting Control System for Providing Comfort Space. In Proceedings of the 2017 IEEE Smartworld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (Smartworld/Scalcom/UIC/ATC/CBDCOM/IOP/SCI), San Francisco, CA, USA, 4–8 August 2017; IEEE: New York, NY, USA, 2017. [Google Scholar]
- Park, J.Y.; Nagy, Z. The Influence of Building Design, Sensor Placement, and Occupant Preferences on Occupant Centered Lighting Control. In Proceedings of the Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience, Atlanta, GA, USA, 17–19 June 2019; Cho, Y.K., Leite, F., Behzadan, A., Wang, C., Eds.; Amer Soc Civil Engineers: New York, NY, USA, 2019; pp. 98–104. [Google Scholar]
- Zhang, S.; Ai, Z.; Lin, Z. Novel Demand-Controlled Optimization of Constant-Air-Volume Mechanical Ventilation for Indoor Air Quality, Durability and Energy Saving. Appl. Energy 2021, 293, 116954. [Google Scholar] [CrossRef]
- Yu, Y.; Liu, M.; Li, H.; Yu, D.; Loftness, V. Synergization of Air Handling Units for High Energy Efficiency in Office Buildings: Implementation Methodology and Performance Evaluation. Energy Build. 2012, 54, 426–435. [Google Scholar] [CrossRef]
- Cespedes-Cubides, A.S.; Jradi, M. A Review of Building Digital Twins to Improve Energy Efficiency in the Building Operational Stage. Energy Inf. 2024, 7, 11. [Google Scholar] [CrossRef]
- Ünalan, B.; Celani, A.; Tanrıvermiş, H.; Bülbül, M.; Ciaramella, A. Impact of Embodied Carbon in the Life Cycle of Buildings on Climate Change for a Sustainable Future. In Proceedings of the 40th IAHS World Congress on Housing—Sustainable Housing Construction, Funchal, Portugal, 16–19 December 2014; Unpublished. [Google Scholar] [CrossRef]
- Schumacher, K. 10 Ways to Reduce Energy Consumption on Construction Sites. Available online: https://spacewell.com/resources/blog/10-ways-to-reduce-energy-consumption-on-construction-sites/ (accessed on 19 March 2025).
- Kong, X.; Xi, C.; Li, H.; Lin, Z. Multi-Parameter Performance Optimization for Whole Year Operation of Stratum Ventilation in Offices. Appl. Energy 2020, 268, 114966. [Google Scholar] [CrossRef]
- Federal Energy Management Program. Energy Efficiency and Indoor Environmental Quality Assessment Guide. Available online: https://www.energy.gov/femp/articles/energy-efficiency-and-indoor-environmental-quality-assessment-guide (accessed on 19 March 2025).
- Rose, J.; Thomsen, K.E.; Balslev-Olesen, O. The Balance between Energy Efficiency and Renewable Energy for District Renovations in Denmark. Sustainability 2022, 14, 13605. [Google Scholar] [CrossRef]
- Khalid, H.; Thaheem, M.J.; Malik, M.S.A.; Musarat, M.A.; Alaloul, W.S. Reducing Cooling Load and Lifecycle Cost for Residential Buildings: A Case of Lahore, Pakistan. Int. J. Life Cycle Assess. 2021, 26, 2355–2374. [Google Scholar] [CrossRef]
- Pacheco, R.; Ordóñez, J.; Martínez, G. Energy Efficient Design of Building: A Review. Renew. Sustain. Energy Rev. 2012, 16, 3559–3573. [Google Scholar] [CrossRef]
- Sanzana, M.R.; Abdulrazic, M.O.M.; Wong, J.Y.; Maul, T.; Yip, C.-C. Charging Water Load Prediction for a Thermal-Energy-Storage Air-Conditioner of a Commercial Building with a Multilayer Perceptron. J. Build. Eng. 2023, 75, 107016. [Google Scholar] [CrossRef]
- Ghahramani, A.; Galicia, P.; Lehrer, D.; Varghese, Z.; Wang, Z.; Pandit, Y. Artificial Intelligence for Efficient Thermal Comfort Systems: Requirements, Current Applications and Future Directions. Front. Built Environ. 2020, 6, 49. [Google Scholar] [CrossRef]
- Zhang, C.; Cui, C.; Zhang, Y.; Yuan, J.; Luo, Y.; Gang, W. A Review of Renewable Energy Assessment Methods in Green Building and Green Neighborhood Rating Systems. Energy Build. 2019, 195, 68–81. [Google Scholar] [CrossRef]
- Yang, Z.; Ghahramani, A.; Becerik-Gerber, B. Building Occupancy Diversity and HVAC (Heating, Ventilation, and Air Conditioning) System Energy Efficiency. Energy 2016, 109, 641–649. [Google Scholar] [CrossRef]
- Kader, A. Climate Adapted Façades in Zero-Waste and Cradle to Cradle Buildings–Comparison, Evaluation and Future Recommendations, Eg in Regard to U-Values, G-Values, Photovoltaic Integration, Thermal Performance and Solar Orientation. In Proceedings of the IOP Conference Series: Materials Science and Engineering, Prague, Czech Republic, 15–19 June 2020; IOP Publishing: Bristol, UK, 2020; Volume 960, p. 032105. [Google Scholar]
- Economidou, M.; Todeschi, V.; Bertoldi, P.; D’Agostino, D.; Zangheri, P.; Castellazzi, L. Review of 50 Years of EU Energy Efficiency Policies for Buildings. Energy Build. 2020, 225, 110322. [Google Scholar] [CrossRef]
- Muhamad, W.N.W.; Zain, M.Y.M.; Wahab, N.; Aziz, N.H.A.; Kadir, R.A. Energy Efficient Lighting System Design for Building. In Proceedings of the 2010 International Conference on Intelligent Systems, Modelling and Simulation, Liverpool, UK, 27 January 2010; pp. 282–286. [Google Scholar]
- Hassanizadeh, N.; Noorzai, E. Improving Lighting Efficiency in Existing Art Museums: A Case Study. Facilities 2020, 39, 366–388. [Google Scholar] [CrossRef]
- ul Haq, M.A.; Hassan, M.Y.; Abdullah, H.; Rahman, H.A.; Abdullah, M.P.; Hussin, F.; Said, D.M. A Review on Lighting Control Technologies in Commercial Buildings, Their Performance and Affecting Factors. Renew. Sustain. Energy Rev. 2014, 33, 268–279. [Google Scholar] [CrossRef]
- Verma, N.; Jain, A. Optimized Automatic Lighting Control in a Hotel Building for Energy Efficiency. In Proceedings of the 2018 International Conference on Power Energy, Environment and Intelligent Control (PEEIC), Greater Noida, India, 13–14 April 2018; pp. 168–172. [Google Scholar]
- Hossain, J.; Kadir, F.A.; Hanafi, A.N.; Shareef, H.; Khatib, T.; Baharin, K.A.; Sulaima, M.F. A Review on Optimal Energy Management in Commercial Buildings. Energies 2023, 16, 1609. [Google Scholar] [CrossRef]
- Managing Plug Load Is the Next Challenge for Energy Efficient Buildings. Available online: https://www.automatedbuildings.com/news/may11/articles/legrand/110425022303legrand.html (accessed on 19 March 2025).
- Jenkins, M. Thermal Comfort Basics: What Is ASHRAE 55? Available online: https://www.simscale.com/blog/what-is-ashrae-55-thermal-comfort/ (accessed on 19 March 2025).
- Even, M.; Juritsch, E.; Richter, M. Measurement of very volatile organic compounds (VVOCs) in indoor air by sorbent-based active sampling: Identifying the gaps towards standardisation. TrAC Trends Anal. Chem. 2021, 140, 116265. [Google Scholar] [CrossRef]
- Fanger, P.O. Thermal Comfort. Analysis and Applications in Environmental Engineering; Danish Technical Press: Copenhagen, Denmark, 1970. [Google Scholar]
- ISO 7730:1984; Moderate thermal environments —Determination of the PMV and PPD indices and specification of the conditions for thermal comfort. Available online: https://www.iso.org/standard/14566.html#:~:text=ISO%207730%3A1984%20%2D%20Moderate%20thermal,the%20conditions%20for%20thermal%20comfort (accessed on 19 March 2025).
- Avelino, A.D.; da Silva, L.B.; Souza, E.L. The Influence of the Metabolism in the PMV Model from ISO 7730 (2005). In Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018), Florence, Italy, 26–30 August 2018; Springer: Berlin/Heidelberg, Germany, 2019; Volume II: Safety and Health, Slips, Trips and Falls 20, pp. 54–64. [Google Scholar]
- ANSI/ASHRAE Standard 55-2017; Thermal Environmental Conditions for Human Occupancy. ASHRAE: Peachtree Corners, GA, USA, 2017.
- Halhoul Merabet, G.; Essaaidi, M.; Ben Haddou, M.; Qolomany, B.; Qadir, J.; Anan, M.; Al-Fuqaha, A.; Abid, M.R.; Benhaddou, D. Intelligent Building Control Systems for Thermal Comfort and Energy-Efficiency: A Systematic Review of Artificial Intelligence-Assisted Techniques. Renew. Sustain. Energy Rev. 2021, 144, 110969. [Google Scholar] [CrossRef]
- Iacomussi, P.; Radis, M.; Rossi, G.; Rossi, L. Visual Comfort with LED Lighting. Energy Procedia 2015, 78, 729–734. [Google Scholar] [CrossRef]
- ISO 8995-1:2002; Lighting of work places—Part 1: Indoor. Available online: https://www.iso.org/standard/28857.html (accessed on 19 March 2025).
- Akyurek, E. Lighting Standards for Ships and Energy Efficiency. Trends Chall. Marit. Energy Manag. 2018, 6, 169–182. [Google Scholar] [CrossRef]
- EN 12464-1:2021; Light and lighting—Lighting of work places—Part 1: Indoor work places. Available online: https://standards.iteh.ai/catalog/standards/cen/53fc4ff7-e7df-4ebd-a730-0d5f0ea888e0/en-12464-1-2021 (accessed on 19 March 2025).
- ASHRAE. ANSI/ASHRAE/IES Standard 90.1-2022; Energy Standard for Sites and Buildings Except Low-Rise Residential Buildings. Available online: https://www.ashrae.org/file%20library/technical%20resources/standards%20and%20guidelines/standards%20addenda/90_1_2022_r_20240531.pdf (accessed on 19 March 2025).
- LEED v5|U.S. Green Building Council. Available online: https://www.usgbc.org/leed/v5 (accessed on 19 March 2025).
- US EPA Introduction to Indoor Air Quality. Available online: https://www.epa.gov/indoor-air-quality-iaq/introduction-indoor-air-quality (accessed on 19 March 2025).
- Lewis, A.C.; Allan, J.; Carslaw, D.; Carruthers, D.; Fuller, G.; Harrison, R.; Heal, M.; Nemitz, E.; Reeves, C.; Carslaw, N.; et al. Indoor Air Quality; Zenodo: Geneve, Switzerland, 2022. [Google Scholar]
- Cociorva, S.; Iftene, A. Indoor Air Quality Evaluation in Intelligent Building. Energy Procedia 2017, 112, 261–268. [Google Scholar] [CrossRef]
- Zhu, C.; Li, N. Study on Grey Clustering Model of Indoor Air Quality Indicators. Procedia Eng. 2017, 205, 2815–2822. [Google Scholar] [CrossRef]
- ANSI/ASHRAE Standard 62.1-2022; Ventilation and Acceptable Indoor Air Quality. Available online: https://www.ashrae.org/technical-resources/bookstore/standards-62-1-62-2 (accessed on 19 March 2025).
- Enhanced Indoor Air Quality in Commercial & Amp; Institutional Buildings–ProQuest. Available online: https://www.proquest.com/openview/3bc8a4b1d8eb52e71b648a1638e9ba01/1?cbl=41118&pq-origsite=gscholar (accessed on 27 March 2025).
- ISO 16000-6:2021; Indoor air Part 6: Determination of organic compounds (VVOC, VOC, SVOC) in indoor and test chamber air by active sampling on sorbent tubes, thermal desorption and gas chromatography using MS or MS FID. Available online: https://www.iso.org/standard/73522.html (accessed on 19 March 2025).
- Kolokotsa, D.; Diakaki, C.; Grigoroudis, E.; Stavrakakis, G.; Kalaitzakis, K. Decision Support Methodologies on the Energy Efficiency and Energy Management in Buildings. Adv. Build. Energy Res. 2009, 3, 121–146. [Google Scholar] [CrossRef]
- Han, F.; Liu, B.; Wang, Y.; Dermentzis, G.; Cao, X.; Zhao, L.; Pfluger, R.; Feist, W. Verifying of the Feasibility and Energy Efficiency of the Largest Certified Passive House Office Building in China: A Three-Year Performance Monitoring Study. J. Build. Eng. 2022, 46, 103703. [Google Scholar] [CrossRef]
- Wang, Y.; Hu, B.; Meng, X.; Xiao, R. A Comprehensive Review on Technologies for Achieving Zero-Energy Buildings. Sustainability 2024, 16, 10941. [Google Scholar] [CrossRef]
- Scheuring, L.; Weller, B. An Investigation of Ventilation Control Strategies for Louver Windows in Different Climate Zones. Int. J. Vent. 2021, 20, 226–235. [Google Scholar] [CrossRef]
- Tang, L.; Ai, Z.; Song, C.; Zhang, G.; Liu, Z. A Strategy to Maximally Utilize Outdoor Air for Indoor Thermal Environment. Energies 2021, 14, 3987. [Google Scholar] [CrossRef]
- Duan, Z.; Sun, Y.; Wang, M.; Hu, R.; Dong, X. Evaluation of Mixed-Mode Ventilation Thermal Performance and Energy Saving Potential from Retrofitting a Beijing Office Building. Buildings 2022, 12, 793. [Google Scholar] [CrossRef]
- Park, K.-Y.; Woo, D.-O.; Leigh, S.-B.; Junghans, L. Impact of Hybrid Ventilation Strategies in Energy Savings of Buildings: In Regard to Mixed-Humid Climate Regions. Energies 2022, 15, 1960. [Google Scholar] [CrossRef]
- Ran, J.; Xiong, K.; Dou, M.; Zhong, H.; Feng, Y.; Tang, M.; Yang, Z. Effect of Window Openable Area and Shading on Indoor Thermal Comfort and Energy Efficiency in Residential Buildings with Various Operating Modes. Atmosphere 2022, 13, 2020. [Google Scholar] [CrossRef]
- Liu, Y.; Wang, W.; Li, Z.; Song, J.; Fang, Z.; Pang, D.; Chen, Y. Daylighting Performance and Thermal Comfort Performance Analysis of West-Facing External Shading for School Office Buildings in Cold and Severe Cold Regions of China. Sustainability 2023, 15, 14458. [Google Scholar] [CrossRef]
- Pérez-Carramiñana, C.; González-Avilés, Á.B.; Castilla, N.; Galiano-Garrigós, A. Influence of Sun Shading Devices on Energy Efficiency, Thermal Comfort and Lighting Comfort in a Warm Semi-Arid Dry Mediterranean Climate. Buildings 2024, 14, 556. [Google Scholar] [CrossRef]
- Iskandar, L.; Faubel, C.; Bay-Sahin, E.; Martinez-Molina, A.; Toker Beeson, S. Climate Change Impact on Natural Ventilation Cooling Effectiveness Using CFD Simulations in Low Thermal Mass Historic Buildings. Int. J. Archit. Herit. 2025, 1–25. [Google Scholar] [CrossRef]
- Mariano-Hernández, D.; Hernández-Callejo, L.; Zorita-Lamadrid, A.; Duque-Pérez, O.; García, F.S. A Review of Strategies for Building Energy Management System: Model Predictive Control, Demand Side Management, Optimization, and Fault Detect & Diagnosis. J. Build. Eng. 2021, 33, 101692. [Google Scholar] [CrossRef]
- Prakash, N.K.; Vadana, D.P. Machine Learning Based Residential Energy Management System. In Proceedings of the 2017 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Coimbatore, India, 14–16 December 2017; pp. 1–4. [Google Scholar]
- Tan, K.K.; Huang, S.; Ferdous, R. Robust Self-Tuning PID Controller for Nonlinear Systems. In Proceedings of the IECON’01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243), Denver, CO, USA, 2–29 November 2001; Volume 1, pp. 758–763. [Google Scholar]
- Wang, S. Intelligent Buildings and Building Automation; Routledge: London, UK, 2009. [Google Scholar]
- Tien, P.W.; Wei, S.; Darkwa, J.; Wood, C.; Calautit, J.K. Machine Learning and Deep Learning Methods for Enhancing Building Energy Efficiency and Indoor Environmental Quality—A Review. Energy AI 2022, 10, 100198. [Google Scholar] [CrossRef]
- Kolokotsa, D.; Tsiavos, D.; Stavrakakis, G.S.; Kalaitzakis, K.; Antonidakis, E. Advanced Fuzzy Logic Controllers Design and Evaluation for Buildings’ Occupants Thermal–Visual Comfort and Indoor Air Quality Satisfaction. Energy Build. 2001, 33, 531–543. [Google Scholar] [CrossRef]
- Hussain, S.; Gabbar, H.A.; Bondarenko, D.; Musharavati, F.; Pokharel, S. Comfort-Based Fuzzy Control Optimization for Energy Conservation in HVAC Systems. Control. Eng. Pract. 2014, 32, 172–182. [Google Scholar] [CrossRef]
- Drgoňa, J.; Arroyo, J.; Cupeiro Figueroa, I.; Blum, D.; Arendt, K.; Kim, D.; Ollé, E.P.; Oravec, J.; Wetter, M.; Vrabie, D.L.; et al. All You Need to Know about Model Predictive Control for Buildings. Annu. Rev. Control 2020, 50, 190–232. [Google Scholar] [CrossRef]
- Moradzadeh, A.; Mohammadi-Ivatloo, B.; Abapour, M.; Anvari-Moghaddam, A.; Roy, S.S. Heating and Cooling Loads Forecasting for Residential Buildings Based on Hybrid Machine Learning Applications: A Comprehensive Review and Comparative Analysis. IEEE Access 2022, 10, 2196–2215. [Google Scholar] [CrossRef]
- Wani, M.; Swain, A.; Ukil, A. Intelligent Controller for Thermal Comfort Management in Buildings. In Proceedings of the IECON 2021–47th Annual Conference of the IEEE Industrial Electronics Society, Toronto, ON, Canada, 13 October 2021; IEEE: New York, NY, USA, 2021; pp. 1–6. [Google Scholar]
- Xiong, L.; Yao, Y. Study on an Adaptive Thermal Comfort Model with K-Nearest-Neighbors (KNN) Algorithm. Build. Environ. 2021, 202, 108026. [Google Scholar] [CrossRef]
- Feng, C.W.; Chen, Y.J.; Yu, H.Y.; Lu, X.; Bhattacharya, S.; Sharma, H.; Adetola, V.; O’Neill, Z. Sensor Impact Evaluation in Commercial Buildings: The Case of Occupancy-Centric Controls. Energy Build. 2022, 267, 112134. [Google Scholar] [CrossRef]
- Wei, S.; Tien, P.W.; Wu, Y.; Calautit, J.K. A Coupled Deep Learning-Based Internal Heat Gains Detection and Prediction Method for Energy-Efficient Office Building Operation. J. Build. Eng. 2022, 47, 103778. [Google Scholar] [CrossRef]
- Yang, S.; Wan, M.P. Machine-Learning-Based Model Predictive Control with Instantaneous Linearization—A Case Study on an Air-Conditioning and Mechanical Ventilation System. Appl. Energy 2022, 306, 118041. [Google Scholar] [CrossRef]
- Kim, J.; Yoon, S. Virtual PMV Sensor towards Smart Thermostats: Comparison of Modeling Approaches Using Intrusive Data. Energy Build. 2023, 301, 113695. [Google Scholar] [CrossRef]
- Mao, Y.; Yu, J.; Zhang, N.; Zhou, M.; Wang, M. Prediction of Thermal Comfort Indoors and Cooling Loads Based on Reasonable Zoning Using the Improved HHO with Multi-Strategy Fusion-FENN Algorithm. Build. Environ. 2023, 245, 110944. [Google Scholar] [CrossRef]
- Mork, M.; Redder, F.; Xhonneux, A.; Müller, D. Real-World Implementation and Evaluation of a Model Predictive Control Framework in an Office Space. J. Build. Eng. 2023, 78, 107619. [Google Scholar] [CrossRef]
- Spagkakas, C.; Stimoniaris, D.; Tsiamitros, D.; Spagkakas, C.; Stimoniaris, D.; Tsiamitros, D. Efficient Demand Side Management Using a Novel Decentralized Building Automation Algorithm. Energies 2021, 16, 6852. [Google Scholar] [CrossRef]
- Zhang, J.; Xiao, F.; Li, A.; Ma, T.; Xu, K.; Zhang, H.; Yan, R.; Fang, X.; Li, Y.; Wang, D. Graph Neural Network-Based Spatio-Temporal Indoor Environment Prediction and Optimal Control for Central Air-Conditioning Systems. Build. Environ. 2023, 242, 110600. [Google Scholar] [CrossRef]
- Zhao, D.; Watari, D.; Ozawa, Y.; Taniguchi, I.; Suzuki, T.; Shimoda, Y.; Onoye, T. Data-Driven Online Energy Management Framework for HVAC Systems: An Experimental Study. Appl. Energy 2023, 352, 121921. [Google Scholar] [CrossRef]
- Jang, Y.; Park, W. Reinforcement Learning-Based HVAC System Operation Under Limited Data Acquisition. In Proceedings of the 2024 13th International Conference on Renewable Energy Research and Applications (ICRERA), Nagasaki, Japan, 9 November 2024; IEEE: New York, NY, USA; pp. 1142–1146. [Google Scholar]
- Carrico, A.R.; Riemer, M. Motivating Energy Conservation in the Workplace: An Evaluation of the Use of Group-Level Feedback and Peer Education. J. Environ. Psychol. 2011, 31, 1–13. [Google Scholar] [CrossRef]
- Udom, S.U. Exploring Thermal Comfort Band for Healthcare Workers in Remote Clinics in Hot and Arid Climates: An Approach for Building Performance Improvement. In Proceedings of the Building Simulation Conference proceedings, Loughborough, UK, 21–22 September 2020; Corrado, V., Fabrizio, E., Gasparella, A., Patuzzi, F., Eds.; Int Building Performance Simulation Assoc-Ibpsa: Toronto, ON, Canada, 2020; pp. 2473–2481. [Google Scholar]
- Zhao, T.; Zhang, C.; Xu, J.; Wu, Y.; Ma, L. Data-Driven Correlation Model between Human Behavior and Energy Consumption for College Teaching Buildings in Cold Regions of China. J. Build. Eng. 2021, 38, 102093. [Google Scholar] [CrossRef]
- Zhou, Y.; Su, Y.; Xu, Z.; Wang, X.; Wu, J.; Guan, X. A Hybrid Physics-Based/Data-Driven Model for Personalized Dynamic Thermal Comfort in Ordinary Office Environment. Energy Build. 2021, 238, 110790. [Google Scholar] [CrossRef]
- Pinzon Amorocho, J.A.; Hartmann, T.; Hahn, J.; Heiler, S.; Kane, M.B.; Park, S.; Jensch, W. The Information Gap in Occupant-Centric Building Operations: Lessons Learned from Interviews with Building Operators in Germany. Front. Built Environ. 2023, 8, 838859. [Google Scholar] [CrossRef]
- Ruiz, S.N.; Day, J.K.; Govertsen, K.; Kane, M. Communication Breakdown: Energy Efficiency Recommendations to Address the Disconnect between Building Operators and Occupants. Energy Res. Soc. Sci. 2022, 91, 102719. [Google Scholar] [CrossRef]
- Pérez-Carramiñana, C.; González-Avilés, Á.B.; Castilla, N.; Galiano-Garrigós, A.; Gnecco, V.M.; Vittori, F.; Pisello, A.L. Digital Twins for Decoding Human-Building Interaction in Multi-Domain Test-Rooms for Environmental Comfort and Energy Saving via Graph Representation. Energy Build. 2023, 279, 112652. [Google Scholar] [CrossRef]
- Yu, H.; Xu, X. Reinforcement Learning for Occupant Behavior Modeling in Public Buildings: Why, What and How? J. Build. Eng. 2024, 96, 110491. [Google Scholar] [CrossRef]
- Benndorf, G.A.; Wystrcil, D.; Réhault, N. Energy Performance Optimization in Buildings: A Review on Semantic Interoperability, Fault Detection, and Predictive Control. Appl. Phys. Rev. 2018, 5, 041501. [Google Scholar] [CrossRef]
- Saidur, R. Energy Consumption, Energy Savings, and Emission Analysis in Malaysian Office Buildings. Energy Policy 2009, 37, 4104–4113. [Google Scholar] [CrossRef]
- Shen, L.; He, B.; Jiao, L.; Song, X.; Zhang, X. Research on the Development of Main Policy Instruments for Improving Building Energy-Efficiency. J. Clean. Prod. 2016, 112, 1789–1803. [Google Scholar] [CrossRef]
- Tahir, I.; Nasir, A.; Algethami, A. Optimal Control Policy for Energy Management of a Commercial Bank. Energies 2022, 15, 2112. [Google Scholar] [CrossRef]
Category | Keywords |
---|---|
Passive strategies | TS = ((“energy efficiency” OR “energy conservation” OR “energy saving”) AND (building OR buildings) AND (“passive design” OR “passive strategy” OR “passive strategies” OR “passive cooling” OR “passive heating” OR “natural ventilation” OR “passive lighting”) AND (“daylighting” OR “building envelope” OR “insulation” OR “shading”) AND (“method” OR “case study” OR “simulation” OR “field experiment” OR “technology”) AND (“indoor environment” OR “indoor environmental quality” OR “occupant comfort” OR “thermal comfort” OR “lighting comfort” OR “indoor air quality”)) |
Operation control optimization strategies | TS = ((“energy efficiency” OR “energy management” OR “energy conservation”) AND (“operational phase” OR “building operation”) AND (“control” OR “smart control” OR “intelligent control” OR “adaptive control” OR “predictive control” OR “automated control” OR “model predictive control” OR “building automation” OR “control system” OR “monitoring system” OR “fault detection” OR “smart building” OR “building management system” OR “BEMS” OR “energy monitoring” OR “detection” OR “prediction” OR “optimization”) AND (building OR buildings) AND (“method” OR “case study” OR “simulation” OR “experiment” OR “field study” OR “approach” OR “framework” OR “technology” OR “application”) AND (“indoor environment” OR “indoor environmental quality” OR “occupant comfort” OR “thermal comfort” OR “lighting comfort” OR “in-door air quality”)) |
Behavioural intervention strategies | TS = ((“energy efficiency” OR “energy management” OR “energy conservation”) AND (building OR buildings) AND (“occupant behaviour” OR “user behaviour” OR “behavioural change” OR “human factors” OR “occupant interaction” OR “occupancy pat-terns” OR “energy awareness” OR “energy feedback” OR “operational policy” OR “education” OR “incentive”) AND (“method” OR “case study” OR “simulation” OR “field experiment” OR “technology”) AND (“indoor environment” OR “indoor environmental quality” OR “occupant comfort” OR “thermal comfort” OR “lighting comfort” OR “in-door air quality” OR “IEQ”)) |
Year | Building Type | Regions | Climates/Seasons | Targets | Objects/ Index | Algorithm/ Approach | Tools | Reference |
---|---|---|---|---|---|---|---|---|
2021 | Office buildings | Spain, Vietnam, Germany | Mediterranean, subtropical and moderate climate zone | Ventilation and indoor air quality | Louvre windows | Energy simulation | EnergyPlus 8.9.0 | [62] |
2021 | Office buildings | China | Five representative climates range from subtropical to frigid regions | Ventilation | Exterior windows | Energy simulation | EnergyPlus 9.5.0 | [63] |
2022 | Office buildings | China | Continental climates (non-heating season) | Cross-ventilation | Windows, doors | Thermal simulation | DesignBuilder v7 | [64] |
2022 | Office buildings | Korea | Mixed-humid climate | Hybrid ventilation and temperature | Whole room | Thermal simulation | EnergyPlus 9.6.0 | [65] |
2022 | Residential Buildings | China | Hot summer and cold winter | Thermal comfort | Window openable area and shading | Cooling energy consumption and thermal simulation | DesignBuilder v6 | [66] |
2023 | School buildings | China | Cold and severe cold | Daylighting and thermal comfort | External shading | Numerical analyses | Ladybug Tools 1.6.0 | [67] |
2024 | School buildings | Spain | Warm semi-arid dry Mediterranean climate | Thermal and lighting comfort | Sun shading devices | On-site measurements, user surveys and computer simulations | DesignBuilder v7 | [68] |
2025 | Historic buildings | USA | Hot–humid climates | Natural ventilation | Whole building | CFD simulations | IES VE 2025 | [69] |
Year | Building Type | Regions | Climates/Seasons | Targets | Objects/ Index | Algorithm/ Approach | Tools | Reference |
---|---|---|---|---|---|---|---|---|
2021 | Commercial buildings | New Zealand | Two weeks of January and July (cold and warm) | Thermal comfort | HVAC | Heuristic intelligent controller | EnergyPlus 9.1.0 | [79] |
2021 | Community buildings | China | Subtropical monsoon climates | Adaptive thermal comfort | PMV model | KNN-based thermal comfort model | N/A | [80] |
2022 | Commercial buildings | China | N/A | Thermal comfort | HVAC, lighting and equipment | Sensors of OCCs | EnergyPlus 9.6.0 | [81] |
2022 | School buildings | UK | N/A | Heat gain detection and prediction | HVAC | Faster R-CNN | IES VE 2021 | [82] |
2022 | Office buildings | Singapore | N/A | Thermal comfort | Air-conditioning and mechanical ventilation system | ML-based MPC with an IL scheme | N/A | [83] |
2023 | Office buildings | Korea | Summer | Thermal comfort | HVAC | Virtual PMV sensor | Python 2020 | [84] |
2023 | Office buildings | China | Summer | Thermal comfort and cooling loads | Temperature, humidity, solar radiation, carbon dioxide, wind direction | Improved neural network (IMFHHO-FENN) | N/A | [85] |
2023 | Office buildings | Germany | N/A | Thermal comfort, daylight transmission and shading control | Temperature, CO2 and illuminance | Modelica-based MPC | JModelica.org 2010 | [86] |
2023 | School buildings | Greece | Winter and summer | Thermal comfort and visual comfort | BMS and microgrid | Decentralized building automation | N/A | [87] |
2023 | Office buildings | Hong Kong | N/A | Indoor environment prediction and optimal control | AHU-VAV system | GNN-RNN | N/A | [88] |
2023 | School buildings | Japan | N/A | Thermal comfort | HVAC | MPC-based HVAC scheduling strategy | Python | [89] |
2024 | Office buildings | Korea | N/A | Thermal comfort | HVAC | Reinforcement learning | N/A | [90] |
Year | Building Type | Regions | Climates/Seasons | Targets | Objects/ Index | Algorithm/ Approach | Tools | Reference |
---|---|---|---|---|---|---|---|---|
2021 | School buildings | China | Annual average, maximum, and minimum temperatures are 11.5, 37.8, and −19.13 °C | Lighting management | Lighting parameters | Correlation model between human behaviour and energy consumption | Equest-3.65 | [93] |
2021 | Office buildings | China | Winter and summer (34 degrees north latitude) | Personalized dynamic thermal comfort | Temperature, humidity, air velocity and radiation temperature | Hybrid physics-based/data-driven model | N/A | [94] |
2022 | Residential buildings | Germany | N/A | Environmental comfort | Preferred temperature and ventilation | OCC strategies | Interviews | [95] |
2022 | Residential buildings | USA, Canada, Brazil, Italy, Germany, Poland, and Singapore | Five climate zones | Occupant comfort | Occupant and operator relationships | OCC strategies | QSR NVivo v10 | [96] |
2023 | School buildings | Italy | N/A | Human comfort and energy behaviour analysis | Actual environmental monitoring data | Digital twin and GNN | Python, Revit | [97] |
2024 | Public buildings | China | N/A | Indoor environment | Occupant behaviour modelling | Reinforcement learning | Semi-structured interviews | [98] |
Main Component | Comfort Indicator | Control Type | Review Strategies |
---|---|---|---|
Building envelope | Thermal comfort/lighting comfort/air quality comfort | Manual Adjustment | Passive strategies/behavioural intervention strategies |
HVAC systems (heating and cooling) | Thermal comfort | Conventional/Predictive/AI | Control optimization |
HVAC systems (ventilation) | Air quality comfort | Conventional/Predictive/AI | Control optimization |
Lighting systems | Lighting comfort | Conventional/Predictive/AI | Control optimization |
Plug loads and appliances | - | Manual Adjustment | Behavioural intervention strategies |
Environmental Characteristics | [19] | [29] | [70] | [37] | [99] | [100] | [25] | [59] | [101] | [76] | [71] | [102] |
---|---|---|---|---|---|---|---|---|---|---|---|---|
Technical implementation | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | - | ✔ | ✔ | ✔ |
Implementation cost | ✔ | - | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | - |
Energy conversation effectiveness | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
Control precision | - | - | - | ✔ | - | - | - | - | - | - | ✔ | ✔ |
System integration | ✔ | - | - | ✔ | ✔ | - | - | - | - | ✔ | ✔ | ✔ |
Adaptability | - | ✔ | ✔ | ✔ | - | - | ✔ | ✔ | ✔ | - | - | ✔ |
Maintenance requirements | ✔ | - | - | - | ✔ | - | - | - | ✔ | - | - | - |
User-friendliness | ✔ | ✔ | ✔ | ✔ | - | - | ✔ | - | ✔ | ✔ | - | - |
Regulatory compliance | - | ✔ | - | ✔ | ✔ | ✔ | - | ✔ | - | - | - | - |
Long-term benefits | ✔ | ✔ | - | ✔ | - | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ | ✔ |
Scalability | ✔ | - | ✔ | ✔ | ✔ | - | - | ✔ | ✔ | ✔ | ✔ | ✔ |
Summary | 8/11 | 6/11 | 6/11 | 10/11 | 7/11 | 5/11 | 6/11 | 6/11 | 7/11 | 7/11 | 7/11 | 7/11 |
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Lin, S.; Zhang, Y.; Chen, X.; Pan, C.; Dong, X.; Xie, X.; Chen, L. Review and Decision-Making Tree for Methods to Balance Indoor Environmental Comfort and Energy Conservation During Building Operation. Sustainability 2025, 17, 7016. https://doi.org/10.3390/su17157016
Lin S, Zhang Y, Chen X, Pan C, Dong X, Xie X, Chen L. Review and Decision-Making Tree for Methods to Balance Indoor Environmental Comfort and Energy Conservation During Building Operation. Sustainability. 2025; 17(15):7016. https://doi.org/10.3390/su17157016
Chicago/Turabian StyleLin, Shan, Yu Zhang, Xuanjiang Chen, Chengzhi Pan, Xianjun Dong, Xiang Xie, and Long Chen. 2025. "Review and Decision-Making Tree for Methods to Balance Indoor Environmental Comfort and Energy Conservation During Building Operation" Sustainability 17, no. 15: 7016. https://doi.org/10.3390/su17157016
APA StyleLin, S., Zhang, Y., Chen, X., Pan, C., Dong, X., Xie, X., & Chen, L. (2025). Review and Decision-Making Tree for Methods to Balance Indoor Environmental Comfort and Energy Conservation During Building Operation. Sustainability, 17(15), 7016. https://doi.org/10.3390/su17157016