An Overview of Emerging and Sustainable Technologies for Increased Energy Efficiency and Carbon Emission Mitigation in Buildings
Abstract
:1. Introduction
2. Uncertainty-Based Design of Building Energy Systems
3. Sustainable Energy Technologies
3.1. Renewable Energy Integration in Buildings
3.2. Thermal Energy Storage
3.3. Heat Pump Technologies
3.4. Thermal Energy Recovery and Sharing
3.5. Retrofits for Increased Energy Efficiency
4. Data-Driven Modeling, Demand Flexibility, and Integrated Control
4.1. Data-Driven Modeling
4.2. Building Demand Flexibility
4.3. Improved Building Control
4.4. Grid-Buildings Integrated Control
5. Conclusions, Future Direction, and Barriers
Author Contributions
Funding
Data Availability Statement
Conflicts of Interest
References
- Embodied Carbon—World Green Building Council. Available online: https://worldgbc.org/advancing-net-zero/embodied-carbon/ (accessed on 28 September 2023).
- Laski, J.; Burrows, V. From Thousands to Billions. Coordinated Action towards 100% Net Zero Carbon Buildings By 2050; World Green Building Council: London, UK, 2017. [Google Scholar]
- Czerwinska, D. Green Building: Improving the Lives of Billions by Helping to Achieve the UN Sustainable Development Goals —World Green Building Council. Available online: https://worldgbc.org/article/green-building-improving-the-lives-of-billions-by-helping-to-achieve-the-un-sustainable-development-goals/ (accessed on 3 July 2023).
- Jensen, S.Ø.; Marszal-Pomianowska, A.; Lollini, R.; Pasut, W.; Knotzer, A.; Engelmann, P.; Stafford, A.; Reynders, G. IEA EBC Annex 67 Energy Flexible Buildings. Energy Build. 2017, 155, 25–34. [Google Scholar] [CrossRef]
- Sustainable Buildings—The United Nations Environment Programme. Available online: https://www.unep.org/explore-topics/resource-efficiency/what-we-do/cities/sustainable-buildings (accessed on 3 July 2023).
- Wei, W.; Skye, H.M. Residential Net-Zero Energy Buildings: Review and Perspective. Renew. Sustain. Energy Rev. 2021, 142, 110859. [Google Scholar]
- Souley Agbodjan, Y.; Wang, J.; Cui, Y.; Liu, Z.; Luo, Z. Bibliometric Analysis of Zero Energy Building Research, Challenges and Solutions. Sol. Energy 2022, 244, 414–433. [Google Scholar] [CrossRef]
- Sartori, I.; Napolitano, A.; Voss, K. Net Zero Energy Buildings: A Consistent Definition Framework. Energy Build. 2012, 48, 220–232. [Google Scholar] [CrossRef]
- Sami, S.; Gholizadeh, M.; Dadpour, D.; Deymi-Dashtebayaz, M. Design and Optimization of a CCHDP System Integrated with NZEB from Energy, Exergy and Exergoeconomic Perspectives. Energy Convers. Manag. 2022, 271, 116347. [Google Scholar] [CrossRef]
- Chegari, B.; Tabaa, M.; Simeu, E.; Moutaouakkil, F.; Medromi, H. An Optimal Surrogate-Model-Based Approach to Support Comfortable and Nearly Zero Energy Buildings Design. Energy 2022, 248, 123584. [Google Scholar] [CrossRef]
- Djunaedy, E.; Van Den Wymelenberg, K.; Acker, B.; Thimmana, H. Oversizing of HVAC System: Signatures and Penalties. Energy Build. 2011, 43, 468–475. [Google Scholar] [CrossRef]
- Wang, S.K. Handbook of Air Conditioning and Refrigeration, 2nd ed.; McGraw-Hill Education: New York, NY, USA, 2001. [Google Scholar]
- Woradechjumroen, D.; Yu, Y.; Li, H.; Yu, D.; Yang, H. Analysis of HVAC System Oversizing in Commercial Buildings through Field Measurements. Energy Build. 2014, 69, 131–143. [Google Scholar] [CrossRef]
- Kang, J.; Wang, S. Robust Optimal Design of Distributed Energy Systems Based on Life-Cycle Performance Analysis Using a Probabilistic Approach Considering Uncertainties of Design Inputs and Equipment Degradations. Appl. Energy 2018, 231, 615–627. [Google Scholar] [CrossRef]
- Li, H.; Wang, S. Coordinated Robust Optimal Design of Building Envelope and Energy Systems for Zero/Low Energy Buildings Considering Uncertainties. Appl. Energy 2020, 265, 114779. [Google Scholar] [CrossRef]
- Zhang, S.; Sun, Y.; Cheng, Y.; Huang, P.; Oladokun, M.O.; Lin, Z. Response-Surface-Model-Based System Sizing for Nearly/Net Zero Energy Buildings under Uncertainty. Appl. Energy 2018, 228, 1020–1031. [Google Scholar] [CrossRef]
- Zou, B.; Peng, J.; Yin, R.; Li, H.; Li, S.; Yan, J.; Yang, H. Capacity Configuration of Distributed Photovoltaic and Battery System for Office Buildings Considering Uncertainties. Appl. Energy 2022, 319, 119243. [Google Scholar] [CrossRef]
- Huang, P.; Huang, G.; Sun, Y. Uncertainty-Based Life-Cycle Analysis of near-Zero Energy Buildings for Performance Improvements. Appl. Energy 2018, 213, 486–498. [Google Scholar] [CrossRef]
- Shen, L.; Sun, Y. Performance Comparisons of Two System Sizing Approaches for Net Zero Energy Building Clusters under Uncertainties. Energy Build. 2016, 127, 10–21. [Google Scholar] [CrossRef]
- Huang, P.; Lovati, M.; Zhang, X.; Bales, C.; Hallbeck, S.; Becker, A.; Bergqvist, H.; Hedberg, J.; Maturi, L. Transforming a Residential Building Cluster into Electricity Prosumers in Sweden: Optimal Design of a Coupled PV-Heat Pump-Thermal Storage-Electric Vehicle System. Appl. Energy 2019, 255, 113864. [Google Scholar] [CrossRef]
- Fan, G.; Liu, Z.; Liu, X.; Shi, Y.; Wu, D.; Guo, J.; Zhang, S.; Yang, X.; Zhang, Y. Two-Layer Collaborative Optimization for a Renewable Energy System Combining Electricity Storage, Hydrogen Storage, and Heat Storage. Energy 2022, 259, 125047. [Google Scholar] [CrossRef]
- Guo, J.; Zhang, P.; Wu, D.; Liu, Z.; Liu, X.; Zhang, S.; Yang, X.; Ge, H. Multi-Objective Optimization Design and Multi-Attribute Decision-Making Method of a Distributed Energy System Based on Nearly Zero-Energy Community Load Forecasting. Energy 2022, 239, 122124. [Google Scholar] [CrossRef]
- Zhang, S.; Huang, P.; Sun, Y. A Multi-Criterion Renewable Energy System Design Optimization for Net Zero Energy Buildings under Uncertainties. Energy 2016, 94, 654–665. [Google Scholar] [CrossRef]
- Doroudchi, E.; Alanne, K.; Okur, Ö.; Kyyrä, J.; Lehtonen, M. Approaching Net Zero Energy Housing through Integrated EV. Sustain. Cities Soc. 2018, 38, 534–542. [Google Scholar] [CrossRef]
- Liu, Z.; Li, Y.; Fan, G.; Wu, D.; Guo, J.; Jin, G.; Zhang, S.; Yang, X. Co-Optimization of a Novel Distributed Energy System Integrated with Hybrid Energy Storage in Different Nearly Zero Energy Community Scenarios. Energy 2022, 247. [Google Scholar] [CrossRef]
- Mohammadi, F.; Faghihi, F.; Kazemi, A.; Salemi, A.H. The Effect of Multi -Uncertainties on Battery Energy Storage System Sizing in Smart Homes. J. Energy Storage 2022, 52, 104765. [Google Scholar] [CrossRef]
- Park, M.; Wang, Z.; Li, L.; Wang, X. Multi-Objective Building Energy System Optimization Considering EV Infrastructure. Appl. Energy 2023, 332, 120504. [Google Scholar] [CrossRef]
- Liu, Z.; Guo, J.; Li, Y.; Wu, D.; Zhang, S.; Yang, X.; Ge, H.; Cai, Z. Multi-Scenario Analysis and Collaborative Optimization of a Novel Distributed Energy System Coupled with Hybrid Energy Storage for a Nearly Zero-Energy Community. J. Energy Storage 2021, 41, 102992. [Google Scholar] [CrossRef]
- Elkadeem, M.R.; Abido, M.A. Optimal Planning and Operation of Grid-Connected PV/CHP/Battery Energy System Considering Demand Response and Electric Vehicles for a Multi-Residential Complex Building. J. Energy Storage 2023, 72, 108198. [Google Scholar] [CrossRef]
- International Renewable Energy Agency. Global Energy Transformation: A Roadmap to 2050; International Renewable Energy Agency: Masdar City, United Arab Emirates, 2018. [Google Scholar]
- IEA. Funding for Thin Film Technologies for Solar PV—Policies. Available online: https://www.iea.org/policies/13275-funding-for-thin-film-technologies-for-solar-pv (accessed on 9 September 2023).
- Kuşkaya, S.; Bilgili, F.; Muğaloğlu, E.; Khan, K.; Hoque, M.E.; Toguç, N. The Role of Solar Energy Usage in Environmental Sustainability: Fresh Evidence through Time-Frequency Analyses. Renew. Energy 2023, 206, 858–871. [Google Scholar] [CrossRef]
- Statista. Grid-Connected Solar PV Capacity by Select Country. Available online: https://www.statista.com/statistics/665864/solar-capacity-in-selected-countries-by-grid-connection/ (accessed on 28 September 2023).
- Bosu, I.; Mahmoud, H.; Ookawara, S.; Hassan, H. Applied Single and Hybrid Solar Energy Techniques for Building Energy Consumption and Thermal Comfort: A Comprehensive Review. Sol. Energy 2023, 259, 188–228. [Google Scholar] [CrossRef]
- Kwok, K.C.S.; Hu, G. Wind Energy System for Buildings in an Urban Environment. J. Wind Eng. Ind. Aerodyn. 2023, 234, 105349. [Google Scholar] [CrossRef]
- Arnfield, A.J. Two Decades of Urban Climate Research: A Review of Turbulence, Exchanges of Energy and Water, and the Urban Heat Island. Int. J. Climatol. 2003, 23, 1–26. [Google Scholar] [CrossRef]
- NBC10 Philadelphia. What Happened to the Wind Turbines that Twirled above Philadelphia Eagles’ Lincoln Financial Field? Available online: https://www.nbcphiladelphia.com/news/sports/nfl/philadelphia-eagles/what-happened-to-the-wind-turbines-that-twirled-above-philadelphia-eagles-lincoln-financial-field/169875/ (accessed on 30 August 2023).
- Zhu, Y.; Li, W.; Li, J.; Li, H.; Wang, Y.; Li, S. Thermodynamic Analysis and Economic Assessment of Biomass-Fired Organic Rankine Cycle Combined Heat and Power System Integrated with CO2 Capture. Energy Convers. Manag. 2020, 204, 112310. [Google Scholar] [CrossRef]
- Behzadi, A.; Thorin, E.; Duwig, C.; Sadrizadeh, S. Supply-Demand Side Management of a Building Energy System Driven by Solar and Biomass in Stockholm: A Smart Integration with Minimal Cost and Emission. Energy Convers. Manag. 2023, 292, 117420. [Google Scholar] [CrossRef]
- Jiang, T.; Zhang, Y.; Olayiwola, S.; Lau, C.K.; Fan, M.; Ng, K.; Tan, G. Biomass-Derived Porous Carbons Support in Phase Change Materials for Building Energy Efficiency: A Review. Mater. Today Energy 2022, 23, 100905. [Google Scholar] [CrossRef]
- Lancaster University. Lancaster Hydrogen Hub. Available online: https://www.lancaster.ac.uk/energy-lancaster/research/hydrogen-hub/ (accessed on 30 August 2023).
- Wu, Y.; Zhong, L. An Integrated Energy Analysis Framework for Evaluating the Application of Hydrogen-Based Energy Storage Systems in Achieving Net Zero Energy Buildings and Cities in Canada. Energy Convers. Manag. 2023, 286, 117066. [Google Scholar] [CrossRef]
- International Renewable Energy Agency. International Renewable Energy Agency Innovation Outlook: Thermal Energy Storage; International Renewable Energy Agency: Abu Dhabi, United Arab Emirates, 2020; p. 144. [Google Scholar]
- Tunçbilek, E.; Yıldız, Ç.; Arıcı, M.; Ma, Z.; Awan, M.B. Thermal Energy Storage for Enhanced Building Energy Flexibility. Build. Energy Flex. Demand Manag. 2023, 89–119. [Google Scholar] [CrossRef]
- Faisal Ahmed, S.; Khalid, M.; Vaka, M.; Walvekar, R.; Numan, A.; Khaliq Rasheed, A.; Mujawar Mubarak, N. Recent Progress in Solar Water Heaters and Solar Collectors: A Comprehensive Review. Therm. Sci. Eng. Prog. 2021, 25, 100981. [Google Scholar] [CrossRef]
- Zhan, H.; Mahyuddin, N.; Sulaiman, R.; Khayatian, F. Phase Change Material (PCM) Integrations into Buildings in Hot Climates with Simulation Access for Energy Performance and Thermal Comfort: A Review. Constr. Build. Mater. 2023, 397, 132312. [Google Scholar] [CrossRef]
- Asadi, I.; Baghban, M.H.; Hashemi, M.; Izadyar, N.; Sajadi, B. Phase Change Materials Incorporated into Geopolymer Concrete for Enhancing Energy Efficiency and Sustainability of Buildings: A Review. Case Stud. Constr. Mater. 2022, 17, e01162. [Google Scholar] [CrossRef]
- Li, C.; Wen, X.; Cai, W.; Yu, H.; Liu, D. Phase Change Material for Passive Cooling in Building Envelopes: A Comprehensive Review. J. Build. Eng. 2023, 65, 105763. [Google Scholar] [CrossRef]
- Qiao, X.; Kong, X.; Fan, M. Phase Change Material Applied in Solar Heating for Buildings: A Review. J. Energy Storage 2022, 55. [Google Scholar] [CrossRef]
- Schmerse, E.; Ikutegbe, C.A.; Auckaili, A.; Farid, M.M. Using PCM in Two Proposed Residential Buildings in Christchurch, New Zealand. Energies 2020, 13, 6025. [Google Scholar] [CrossRef]
- Gao, Y.; Meng, X. A Comprehensive Review of Integrating Phase Change Materials in Building Bricks: Methods, Performance and Applications. J. Energy Storage 2023, 62, 106913. [Google Scholar] [CrossRef]
- Awan, M.B.; Ma, Z. Building Energy Flexibility: Definitions, Sources, Indicators, and Quantification Methods. Build. Energy Flex. Demand Manag. 2023, 17–40. [Google Scholar] [CrossRef]
- Rosenow, J.; Gibb, D.; Nowak, T.; Lowes, R. Heating up the Global Heat Pump Market. Nat. Energy 2022, 7, 901–904. [Google Scholar] [CrossRef]
- IEA. Installation of about 600 Million Heat Pumps Covering 20% of Buildings Heating Needs Required by 2030—Analysis. Available online: https://www.iea.org/reports/installation-of-about-600-million-heat-pumps-covering-20-of-buildings-heating-needs-required-by-2030 (accessed on 13 August 2023).
- IEA. The Future of Heat Pumps; IEA: Paris, France, 2022. [Google Scholar]
- Energy Efficiency Council. Harnessing Heat Pumps for Net Zero The Role of Heat Pumps in Saving Energy and Cutting Emissions; Energy Efficiency Council: Melbourne, Australia, 2023. [Google Scholar]
- Gaur, A.S.; Fitiwi, D.Z.; Curtis, J. Heat Pumps and Our Low-Carbon Future: A Comprehensive Review. Energy Res. Soc. Sci. 2021, 71, 101764. [Google Scholar] [CrossRef]
- Ma, Z.; Xia, L.; Gong, X.; Kokogiannakis, G.; Wang, S.; Zhou, X. Recent Advances and Development in Optimal Design and Control of Ground Source Heat Pump Systems. Renew. Sustain. Energy Rev. 2020, 131, 110001. [Google Scholar] [CrossRef]
- Cai, W.; Wang, F.; Chen, S.; Chen, C.; Zhang, Y.; Kolditz, O.; Shao, H. Importance of Long-Term Ground-Loop Temperature Variation in Performance Optimization of Ground Source Heat Pump System. Appl. Therm. Eng. 2022, 204, 117945. [Google Scholar] [CrossRef]
- Lee, M.; Cha, D.; Yun, S.; Yoon, S.M.; Kim, Y. Comparative Heating Performance Evaluation of Hybrid Ground-Source Heat Pumps Using Serial and Parallel Configurations with the Application of Ground Heat Exchanger. Energy Convers. Manag. 2021, 229, 113743. [Google Scholar] [CrossRef]
- Bae, S.; Nam, Y. Economic and Environmental Analysis of Ground Source Heat Pump System According to Operation Methods. Geothermics 2022, 101, 102373. [Google Scholar] [CrossRef]
- Lee, M.; Kim, J.; Shin, H.H.; Cho, W.; Kim, Y. CO2 Emissions and Energy Performance Analysis of Ground-Source and Solar-Assisted Ground-Source Heat Pumps Using Low-GWP Refrigerants. Energy 2022, 261, 125198. [Google Scholar] [CrossRef]
- Xu, L.; Pu, L.; Zhang, S.; Li, Y. Hybrid Ground Source Heat Pump System for Overcoming Soil Thermal Imbalance: A Review. Sustain. Energy Technol. Assess. 2021, 44, 101098. [Google Scholar] [CrossRef]
- Abdalla, A.; Mohamed, S.; Bucking, S.; Cotton, J.S. Modeling of Thermal Energy Sharing in Integrated Energy Communities with Micro-Thermal Networks. Energy Build. 2021, 248, 111170. [Google Scholar] [CrossRef]
- Lindhe, J.; Javed, S.; Johansson, D.; Bagge, H. A Review of the Current Status and Development of 5GDHC and Characterization of a Novel Shared Energy System. Sci. Technol. Built Environ. 2022, 28, 595–609. [Google Scholar] [CrossRef]
- Huang, P.; Copertaro, B.; Zhang, X.; Shen, J.; Löfgren, I.; Rönnelid, M.; Fahlen, J.; Andersson, D.; Svanfeldt, M. A Review of Data Centers as Prosumers in District Energy Systems: Renewable Energy Integration and Waste Heat Reuse for District Heating. Appl. Energy 2020, 258, 114109. [Google Scholar] [CrossRef]
- Ebrahimi, K.; Jones, G.F.; Fleischer, A.S. A Review of Data Center Cooling Technology, Operating Conditions and the Corresponding Low-Grade Waste Heat Recovery Opportunities. Renew. Sustain. Energy Rev. 2014, 31, 622–638. [Google Scholar] [CrossRef]
- Giunta, F.; Sawalha, S. Techno-Economic Analysis of Heat Recovery from Supermarket’s CO2 Refrigeration Systems to District Heating Networks. Appl. Therm. Eng. 2021, 193, 117000. [Google Scholar] [CrossRef]
- Mateu-Royo, C.; Sawalha, S.; Mota-Babiloni, A.; Navarro-Esbrí, J. High Temperature Heat Pump Integration into District Heating Network. Energy Convers. Manag. 2020, 210, 112719. [Google Scholar] [CrossRef]
- Reclaim Waste Heat from Rink—Arena Guide. Available online: https://arena-guide.com/go-green/heat-re-claim-heat-recovery/ (accessed on 26 June 2023).
- Pourfarzad, H.; Saremia, M.; Ganjali, M.R. A Novel Tri-Generation Energy System Integrating Solar Energy and Industrial Waste Heat. J. Therm. Eng. 2021, 7, 1067–1078. [Google Scholar] [CrossRef]
- Davies, G.F.; Maidment, G.G.; Tozer, R.M. Using Data Centres for Combined Heating and Cooling: An Investigation for London. Appl. Therm. Eng. 2016, 94, 296–304. [Google Scholar] [CrossRef]
- Khosravi, A.; Laukkanen, T.; Vuorinen, V.; Syri, S. Waste Heat Recovery from a Data Centre and 5G Smart Poles for Low-Temperature District Heating Network. Energy 2021, 218, 119468. [Google Scholar] [CrossRef]
- Kuyumcu, M.E.; Tutumlu, H.; Yumrutaş, R. Performance of a Swimming Pool Heating System by Utilizing Waste Energy Rejected from an Ice Rink with an Energy Storage Tank. Energy Convers. Manag. 2016, 121, 349–357. [Google Scholar] [CrossRef]
- Oró, E.; Allepuz, R.; Martorell, I.; Salom, J. Design and Economic Analysis of Liquid Cooled Data Centres for Waste Heat Recovery: A Case Study for an Indoor Swimming Pool. Sustain. Cities Soc. 2018, 36, 185–203. [Google Scholar] [CrossRef]
- Pan, Q.; Peng, J.; Wang, R. Experimental Study of an Adsorption Chiller for Extra Low Temperature Waste Heat Utilization. Appl. Therm. Eng. 2019, 163, 114341. [Google Scholar] [CrossRef]
- Araya, S.; Wemhoff, A.P.; Jones, G.F.; Fleischer, A.S. Study of a Lab-Scale Organic Rankine Cycle for the Ultra-Low-Temperature Waste Heat Recovery Associated with Data Centers. J. Electron. Packag. Trans. ASME 2021, 143, 021001. [Google Scholar] [CrossRef]
- Wirtz, M.; Kivilip, L.; Remmen, P.; Müller, D. 5th Generation District Heating: A Novel Design Approach Based on Mathematical Optimization. Appl. Energy 2020, 260, 114158. [Google Scholar] [CrossRef]
- Murphy, A.R.; Fung, A.S. Techno-Economic Study of an Energy Sharing Network Comprised of a Data Centre and Multi-Unit Residential Buildings for Cold Climate. Energy Build. 2019, 186, 261–275. [Google Scholar] [CrossRef]
- Zhang, C.; Luo, H.; Wang, Z. An Economic Analysis of Waste Heat Recovery and Utilization in Data Centers Considering Environmental Benefits. Sustain. Prod. Consum. 2022, 31, 127–138. [Google Scholar] [CrossRef]
- Li, H.; Hou, J.; Hong, T.; Ding, Y.; Nord, N. Energy, Economic, and Environmental Analysis of Integration of Thermal Energy Storage into District Heating Systems Using Waste Heat from Data Centres. Energy 2021, 219. [Google Scholar] [CrossRef]
- Li, H.; Hou, J.; Tian, Z.; Hong, T.; Nord, N.; Rohde, D. Optimize Heat Prosumers’ Economic Performance under Current Heating Price Models by Using Water Tank Thermal Energy Storage. Energy 2022, 239, 122103. [Google Scholar] [CrossRef]
- Wang, X.; Li, H.; Wang, Y.; Zhao, J.; Zhu, J.; Zhong, S.; Li, Y. Energy, Exergy, and Economic Analysis of a Data Center Energy System Driven by the CO2 Ground Source Heat Pump: Prosumer Perspective. Energy Convers. Manag. 2021, 232, 113877. [Google Scholar] [CrossRef]
- Energy Retrofit Systems Market Size, Share, Forecast. 2023. Available online: https://www.marketresearchfuture.com/reports/energy-retrofit-systems-market-11758 (accessed on 13 August 2023).
- Ang, Y.Q.; Berzolla, Z.M.; Letellier-Duchesne, S.; Reinhart, C.F. Carbon Reduction Technology Pathways for Existing Buildings in Eight Cities. Nat. Commun. 2023, 14, 1689. [Google Scholar] [CrossRef]
- Alabid, J.; Bennadji, A.; Seddiki, M. A Review on the Energy Retrofit Policies and Improvements of the UK Existing Buildings, Challenges and Benefits. Renew. Sustain. Energy Rev. 2022, 159, 112161. [Google Scholar] [CrossRef]
- Deb, C.; Schlueter, A. Review of Data-Driven Energy Modelling Techniques for Building Retrofit. Renew. Sustain. Energy Rev. 2021, 144, 110990. [Google Scholar] [CrossRef]
- Thrampoulidis, E.; Hug, G.; Orehounig, K. Approximating Optimal Building Retrofit Solutions for Large-Scale Retrofit Analysis. Appl. Energy 2023, 333, 120566. [Google Scholar] [CrossRef]
- Densley Tingley, D. Embed Circular Economy Thinking into Building Retrofit. Commun. Eng. 2022, 1, 28. [Google Scholar] [CrossRef]
- Xu, Y.; Zhou, Y.; Sekula, P.; Ding, L. Machine Learning in Construction: From Shallow to Deep Learning. Dev. Built Environ. 2021, 6, 100045. [Google Scholar] [CrossRef]
- Janiesch, C.; Zschech, P.; Heinrich, K. Machine Learning and Deep Learning. Electron. Mark. 2021, 31, 685–695. [Google Scholar] [CrossRef]
- Wang, Z.; Hong, T.; Piette, M.A. Building Thermal Load Prediction through Shallow Machine Learning and Deep Learning. Appl. Energy 2020, 263. [Google Scholar] [CrossRef]
- 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]
- Muzaffar, S.; Afshari, A. Short-Term Load Forecasts Using LSTM Networks. Energy Procedia 2019, 158, 2922–2927. [Google Scholar] [CrossRef]
- Zhou, Y.; Wang, J.; Liu, Y.; Yan, R.; Ma, Y. Incorporating Deep Learning of Load Predictions to Enhance the Optimal Active Energy Management of Combined Cooling, Heating and Power System. Energy 2021, 233, 121134. [Google Scholar] [CrossRef]
- Guo, W.; Che, L.; Shahidehpour, M.; Wan, X. Machine-Learning Based Methods in Short-Term Load Forecasting. Electr. J. 2021, 34, 106884. [Google Scholar] [CrossRef]
- Feng, C.; Zhang, J.; Zhang, W.; Hodge, B.M. Convolutional Neural Networks for Intra-Hour Solar Forecasting Based on Sky Image Sequences. Appl. Energy 2022, 310, 118438. [Google Scholar] [CrossRef]
- Zhang, W.; Zhou, H.; Bao, X.; Cui, H. Outlet Water Temperature Prediction of Energy Pile Based on Spatial-Temporal Feature Extraction through CNN–LSTM Hybrid Model. Energy 2023, 264, 126190. [Google Scholar] [CrossRef]
- Liang, X.; Chen, S.; Zhu, X.; Jin, X.; Du, Z. Domain Knowledge Decomposition of Building Energy Consumption and a Hybrid Data-Driven Model for 24-h Ahead Predictions. Appl. Energy 2023, 344. [Google Scholar] [CrossRef]
- Zhou, X.; Lin, W.; Kumar, R.; Cui, P.; Ma, Z. A Data-Driven Strategy Using Long Short Term Memory Models and Reinforcement Learning to Predict Building Electricity Consumption. Appl. Energy 2022, 306, 118078. [Google Scholar] [CrossRef]
- Zhou, X.; Du, H.; Sun, Y.; Ren, H.; Cui, P.; Ma, Z. A New Framework Integrating Reinforcement Learning, a Rule-Based Expert System, and Decision Tree Analysis to Improve Building Energy Flexibility. J. Build. Eng. 2023, 71, 106536. [Google Scholar] [CrossRef]
- Li, J.; Niu, H.; Meng, F.; Li, R. Prediction of Short-Term Photovoltaic Power Via Self-Attention-Based Deep Learning Approach. J. Energy Resour. Technol. Trans. ASME 2022, 144, 101301. [Google Scholar] [CrossRef]
- Gangopadhyay, T.; Tan, S.Y.; Jiang, Z.; Sarkar, S. Interpretable Deep Attention Model for Multivariate Time Series Prediction in Building Energy Systems. In Lecture Notes in Computer Science; Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics; Springer Nature: Cham, Switzerland, 2020; Volume 12312 LNCS, pp. 93–101. [Google Scholar]
- Li, D.; Li, J.; Zeng, X.; Stankovic, V.; Stankovic, L.; Xiao, C.; Shi, Q. Transfer Learning for Multi-Objective Non-Intrusive Load Monitoring in Smart Building. Appl. Energy 2023, 329, 120223. [Google Scholar] [CrossRef]
- Chen, L.; Ermis, A.; Meng, F.; Zhang, Y. Meta-Learning of Personalized Thermal Comfort Model and Fast Identification of the Best Personalized Thermal Environmental Conditions. Build. Environ. 2023, 235, 110201. [Google Scholar] [CrossRef]
- Moon, J.; Jung, S.; Park, S.; Hwang, E. Conditional Tabular GaN-Based Two-Stage Data Generation Scheme for Short-Term Load Forecasting. IEEE Access 2020, 8, 205327–205339. [Google Scholar] [CrossRef]
- Ma, Z.; Arıcı, M.; Shahsavar, A. Building Energy Flexibilty and Demand Management; Elsevier: Amsterdam, The Netherlands, 2023; ISBN 9780323995894. [Google Scholar]
- Satchwell, A.; Ann Piette, M.; Khandekar, A.; Granderson, J.; Mims Frick, N.; Hledik, R.; Faruqui, A.; Lam, L.; Ross, S.; Cohen, J.; et al. A National Roadmap for Grid-Interactive Efficient Buildings; Lawrence Berkeley National Lab: Berkeley, CA, USA, 2021.
- Verbeke, S.; Aerts, D.; Reynders, G.; Ma, Y.; Waide, P. Final Report on the Technical Support to the Development of A Smart Readiness Indicator for Buildings; European Commission: Brussels, Belgium, 2020. [Google Scholar]
- Munankarmi, P.; Maguire, J.; Balamurugan, S.P.; Blonsky, M.; Roberts, D.; Jin, X. Community-Scale Interaction of Energy Efficiency and Demand Flexibility in Residential Buildings. Appl. Energy 2021, 298, 117149. [Google Scholar] [CrossRef]
- Ren, H.; Sun, Y.; Albdoor, A.K.; Tyagi, V.V.; Pandey, A.K.; Ma, Z. Improving Energy Flexibility of a Net-Zero Energy House Using a Solar-Assisted Air Conditioning System with Thermal Energy Storage and Demand-Side Management. Appl. Energy 2021, 285. [Google Scholar] [CrossRef]
- Awan, M.B.; Sun, Y.; Lin, W.; Ma, Z. A Framework to Formulate and Aggregate Performance Indicators to Quantify Building Energy Flexibility. Appl. Energy 2023, 349, 121590. [Google Scholar] [CrossRef]
- Jensen, S.Ø.; Marszal, A.J.; Johra, H.; Weiss, T.; Knotzer, A.; Kazmi, H.; Vigna, I.; Pernetti, R.; Le Dréau, J.; Zhang, K.; et al. Characterization of Energy Flexibility in Buildings; Energy in Buildings and Communities Programme Annex 67 Energy Flexible Buildings; International Energy Agency: Paris, France, 2019. [Google Scholar]
- Langner, R.; Granderson, J.; Crowe, E. Quantifying the Value of Grid-Interactive Efficient Buildings through Field Study; National Renewable Energy Lab: Golden, CO, USA, 2022.
- Ruggiero, S.; Francesca De Masi, R.; Assimakopoulos, M.N.; Peter Vanoli, G. Energy Saving through Building Automation Systems: Experimental and Numerical Study of a Smart Glass with Liquid Crystal and Its Control Logics in Summertime. Energy Build. 2022, 273, 112403. [Google Scholar] [CrossRef]
- Langevin, J.; Harris, C.B.; Satre-Meloy, A.; Chandra-Putra, H.; Speake, A.; Present, E.; Adhikari, R.; Wilson, E.J.H.; Satchwell, A.J. US Building Energy Efficiency and Flexibility as an Electric Grid Resource. Joule 2021, 5, 2102–2128. [Google Scholar] [CrossRef]
- Ochs, M. How Lighting Control Systems Contribute to Flexible, Future-Proof Buildings. Available online: https://www.csemag.com/articles/how-lighting-control-systems-contribute-to-flexible-future-proof-buildings/ (accessed on 12 September 2023).
- Tang, H.; Wang, S. Game-Theoretic Optimization of Demand-Side Flexibility Engagement Considering the Perspectives of Different Stakeholders and Multiple Flexibility Services. Appl. Energy 2023, 332, 120550. [Google Scholar] [CrossRef]
- Zhou, Y.; Wang, J.; Dong, F.; Qin, Y.; Ma, Z.; Ma, Y.; Li, J. Novel Flexibility Evaluation of Hybrid Combined Cooling, Heating and Power System with an Improved Operation Strategy. Appl. Energy 2021, 300, 117358. [Google Scholar] [CrossRef]
- Finck, C.; Li, R.; Zeiler, W. Optimal Control of Demand Flexibility under Real-Time Pricing for Heating Systems in Buildings: A Real-Life Demonstration. Appl. Energy 2020, 263, 114671. [Google Scholar] [CrossRef]
- Afroz, Z.; Shafiullah, G.M.; Urmee, T.; Higgins, G. Modeling Techniques Used in Building HVAC Control Systems: A Review. Renew. Sustain. Energy Rev. 2018, 83, 64–84. [Google Scholar] [CrossRef]
- Fiorentini, M.; Wall, J.; Ma, Z.; Braslavsky, J.H.; Cooper, P. Hybrid model predictive control of a residential HVAC system with on-site thermal energy generation and storage. Appl. Energy 2017, 187, 465–479. [Google Scholar] [CrossRef]
- Merema, B.; Saelens, D.; Breesch, H. Demonstration of an MPC Framework for All-Air Systems in Non-Residential Buildings. Build. Environ. 2022, 217, 109053. [Google Scholar] [CrossRef]
- Wei, T.; Wang, Y.; Zhu, Q. Deep Reinforcement Learning for Building HVAC Control. In Proceedings of the Design Automation Conference, Austin, TX, USA, 18 June 2017; Institute of Electrical and Electronics Engineers Inc.: Piscataway, NJ, USA, 2017; Volume 12828. [Google Scholar]
- Azuatalam, D.; Lee, W.L.; de Nijs, F.; Liebman, A. Reinforcement Learning for Whole-Building HVAC Control and Demand Response. Energy AI 2020, 2, 100020. [Google Scholar] [CrossRef]
- Yu, L.; Sun, Y.; Xu, Z.; Shen, C.; Yue, D.; Jiang, T.; Guan, X. Multi-Agent Deep Reinforcement Learning for HVAC Control in Commercial Buildings. IEEE Trans. Smart Grid 2020, 12, 407–419. [Google Scholar] [CrossRef]
- Coulson, J.; Lygeros, J.; Dorfler, F. Data-Enabled Predictive Control: In the Shallows of the DeePC. In Proceedings of the 2019 18th European Control Conference, ECC 2019, Naples, Italy, 25–28 June 2019; pp. 307–312. [Google Scholar] [CrossRef]
- Chinde, V.; Lin, Y.; Ellis, M.J. Data-Enabled Predictive Control for Building HVAC Systems. J. Dyn. Syst. Meas. Control Trans. ASME 2022, 144, 081001. [Google Scholar] [CrossRef]
- Bajaj, M.; Singh, A.K. Grid Integrated Renewable DG Systems: A Review of Power Quality Challenges and State-of-the-Art Mitigation Techniques. Int. J. Energy Res. 2020, 44, 26–69. [Google Scholar] [CrossRef]
- Naderi, Y.; Hosseini, S.H.; Ghassem Zadeh, S.; Mohammadi-Ivatloo, B.; Vasquez, J.C.; Guerrero, J.M. An Overview of Power Quality Enhancement Techniques Applied to Distributed Generation in Electrical Distribution Networks. Renew. Sustain. Energy Rev. 2018, 93, 201–214. [Google Scholar] [CrossRef]
- Norouzi, F.; Hoppe, T.; Elizondo, L.R.; Bauer, P. A Review of Socio-Technical Barriers to Smart Microgrid Development. Renew. Sustain. Energy Rev. 2022, 167, 112674. [Google Scholar] [CrossRef]
- ACEEE. Grid-Interactive Efficient Buildings Are the Future, and Utilities Can Help Lead the Way. Available online: https://www.aceee.org/blog/2019/11/grid-interactive-efficient-buildings (accessed on 11 July 2023).
- Tumminia, G.; Guarino, F.; Longo, S.; Aloisio, D.; Cellura, S.; Sergi, F.; Brunaccini, G.; Antonucci, V.; Ferraro, M. Grid Interaction and Environmental Impact of a Net Zero Energy Building. Energy Convers. Manag. 2020, 203. [Google Scholar] [CrossRef]
- Lagrange, A.; de Simón-Martín, M.; González-Martínez, A.; Bracco, S.; Rosales-Asensio, E. Sustainable Microgrids with Energy Storage as a Means to Increase Power Resilience in Critical Facilities: An Application to a Hospital. Int. J. Electr. Power Energy Syst. 2020, 119, 105865. [Google Scholar] [CrossRef]
- Huo, X.; Dong, J.; Cui, B.; Liu, B.; Lian, J.; Liu, M. Two-Level Decentralized-Centralized Control of Distributed Energy Resources in Grid-Interactive Efficient Buildings. IEEE Control Syst. Lett. 2022, 7, 997–1002. [Google Scholar] [CrossRef]
- Rastegarpour, S.; Ferrarini, L. Energy Management in Buildings: Lessons Learnt for Modeling and Advanced Control Design. Front. Energy Res. 2022, 10, 899866. [Google Scholar] [CrossRef]
- Stamatescu, G.; Stamatescu, I.; Arghira, N.; Calofir, V.; Făgărăşan, I. Building Cyber-Physical Energy Systems. arXiv 2016, arXiv:1605.06903. [Google Scholar]
- Razmara, M.; Bharati, G.R.; Hanover, D.; Shahbakhti, M.; Paudyal, S.; Robinett, R.D. Building-to-Grid Predictive Power Flow Control for Demand Response and Demand Flexibility Programs. Appl. Energy 2017, 203, 128–141. [Google Scholar] [CrossRef]
- Al-Ali, A.R.; El-Hag, A.; Bahadiri, M.; Harbaji, M.; Ali El Haj, Y. Smart Home Renewable Energy Management System. Energy Procedia 2011, 12, 120–126. [Google Scholar] [CrossRef]
- Palma-Behnke, R.; Benavides, C.; Aranda, E.; Llanos, J.; Sáez, D. Energy Management System for a Renewable Based Microgrid with a Demand Side Management Mechanism. In Proceedings of the IEEE SSCI 2011—Symposium Series on Computational Intelligence—CIASG 2011: 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid, Paris, France, 11–15 April 2011; pp. 131–138. [Google Scholar] [CrossRef]
- Giorgos, G.S.; Christodoulides, P.; Kalogirou, S.A. Optimizing the energy storage schedule of a battery in a PV grid-connected nZEB using linear programming. Energy 2020, 208, 118177. [Google Scholar]
- Abedi, S.; Alimardani, A.; Gharehpetian, G.B.; Riahy, G.H.; Hosseinian, S.H. A Comprehensive Method for Optimal Power Management and Design of Hybrid RES-Based Autonomous Energy Systems. Renew. Sustain. Energy Rev. 2012, 16, 1577–1587. [Google Scholar] [CrossRef]
- Tavakoli, M.; Shokridehaki, F.; Funsho Akorede, M.; Marzband, M.; Vechiu, I.; Pouresmaeil, E. CVaR-Based Energy Management Scheme for Optimal Resilience and Operational Cost in Commercial Building Microgrids. Int. J. Electr. Power Energy Syst. 2018, 100, 1–9. [Google Scholar] [CrossRef]
- Marzband, M.; Fouladfar, M.H.; Akorede, M.F.; Lightbody, G.; Pouresmaeil, E. Framework for Smart Transactive Energy in Home-Microgrids Considering Coalition Formation and Demand Side Management. Sustain. Cities Soc. 2018, 40, 136–154. [Google Scholar] [CrossRef]
- Marzband, M.; Azarinejadian, F.; Savaghebi, M.; Pouresmaeil, E.; Guerrero, J.M.; Lightbody, G. Smart Transactive Energy Framework in Grid-Connected Multiple Home Microgrids under Independent and Coalition Operations. Renew. Energy 2018, 126, 95–106. [Google Scholar] [CrossRef]
- Bilgin, E.; Caramanis, M.C.; Paschalidis, I.C.; Cassandras, C.G. Provision of Regulation Service by Smart Buildings. IEEE Trans. Smart Grid 2016, 7, 1683–1693. [Google Scholar] [CrossRef]
- Taha, A.F.; Gatsis, N.; Dong, B.; Pipri, A.; Li, Z. Buildings-to-Grid Integration Framework. IEEE Trans. Smart Grid 2019, 10, 1237–1249. [Google Scholar] [CrossRef]
- Mirakhorli, A.; Dong, B. Model Predictive Control for Building Loads Connected with a Residential Distribution Grid. Appl. Energy 2018, 230, 627–642. [Google Scholar] [CrossRef]
- Fan, C.; Huang, G.; Sun, Y. A Collaborative Control Optimization of Grid-Connected Net Zero Energy Buildings for Performance Improvements at Building Group Level. Energy 2018, 164, 536–549. [Google Scholar] [CrossRef]
- Clastres, C.; Ha Pham, T.T.; Wurtz, F.; Bacha, S. Ancillary Services and Optimal Household Energy Management with Photovoltaic Production. Energy 2010, 35, 55–64. [Google Scholar] [CrossRef]
Control Strategies | Strengths | Weaknesses |
---|---|---|
Rule-based control | Easy to implement and understand; and can enable safety rules to the control system. | Low flexibility to change setpoints to react to demand variation; and cannot provide global optimal solutions. |
Model predictive control | It enables safety rules; reacts prior to variation to improve stability; and has high flexibility to meet control requirements. | High computational costs; and relies on accurate models for prediction. |
Reinforcement learning | Model-free data-driven method; does not require much information for model development; and requires low reaction time for decision-making once trained. | Requires long training time and large training dataset; and doesn’t enable safety rules. |
Data-enabled predictive control | Does not require a large training dataset; and has high stability for nonlinear time-varying systems. | Has not been widely validated for HVAC control; and the efficiency of the model relies on the quality of the training data. |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Ma, Z.; Awan, M.B.; Lu, M.; Li, S.; Aziz, M.S.; Zhou, X.; Du, H.; Sha, X.; Li, Y. An Overview of Emerging and Sustainable Technologies for Increased Energy Efficiency and Carbon Emission Mitigation in Buildings. Buildings 2023, 13, 2658. https://doi.org/10.3390/buildings13102658
Ma Z, Awan MB, Lu M, Li S, Aziz MS, Zhou X, Du H, Sha X, Li Y. An Overview of Emerging and Sustainable Technologies for Increased Energy Efficiency and Carbon Emission Mitigation in Buildings. Buildings. 2023; 13(10):2658. https://doi.org/10.3390/buildings13102658
Chicago/Turabian StyleMa, Zhenjun, Muhammad Bilal Awan, Menglong Lu, Shengteng Li, Muhammad Shahbaz Aziz, Xinlei Zhou, Han Du, Xinyi Sha, and Yixuan Li. 2023. "An Overview of Emerging and Sustainable Technologies for Increased Energy Efficiency and Carbon Emission Mitigation in Buildings" Buildings 13, no. 10: 2658. https://doi.org/10.3390/buildings13102658