Submitted Successfully!
To reward your contribution, here is a gift for you: A free trial for our video production service.
Thank you for your contribution! You can also upload a video entry or images related to this topic.
Version Summary Created by Modification Content Size Created at Operation
1
Pedro Moura
 -- 1450 2023-09-06 10:46:06 |
2 format correct
Catherine Yang
 Meta information modification 1450 2023-09-06 10:55:46 |

Video Upload Options

Do you have a full video?
Send video materials Upload full video

Confirm

Are you sure to Delete?
Yes No
Cite
If you have any further questions, please contact Encyclopedia Editorial Office.
Pedram, O.; Asadi, E.; Chenari, B.; Moura, P.; Gameiro Da Silva, M. Managing Energy Flexibility Resources in Buildings. Encyclopedia. Available online: https://encyclopedia.pub/entry/48873 (accessed on 29 August 2024).
Pedram O, Asadi E, Chenari B, Moura P, Gameiro Da Silva M. Managing Energy Flexibility Resources in Buildings. Encyclopedia. Available at: https://encyclopedia.pub/entry/48873. Accessed August 29, 2024.
Pedram, Omid, Ehsan Asadi, Behrang Chenari, Pedro Moura, Manuel Gameiro Da Silva. "Managing Energy Flexibility Resources in Buildings" Encyclopedia, https://encyclopedia.pub/entry/48873 (accessed August 29, 2024).
Pedram, O., Asadi, E., Chenari, B., Moura, P., & Gameiro Da Silva, M. (2023, September 06). Managing Energy Flexibility Resources in Buildings. In Encyclopedia. https://encyclopedia.pub/entry/48873
Pedram, Omid, et al. "Managing Energy Flexibility Resources in Buildings." Encyclopedia. Web. 06 September, 2023.
Managing Energy Flexibility Resources in Buildings
Edit
This entry is adapted from the peer-reviewed paper 10.3390/en16176111

The integration of renewable energy and flexible energy sources in buildings brings numerous benefits. However, the integration of new technologies has increased the complexity and despite the progress of optimization algorithms and technologies, new research challenges emerge. Multi-objective optimization is crucial to enhance the utilization of flexible resources within individual buildings and communities. 

energy optimization buildings energy communities building energy management systems

1. Introduction

It is expected that the building sector will contribute to 30% of global CO2 emissions and consume 40% of the total energy by 2030 [1], leading to a significant global impact. The “Clean Energy for All Europeans” package [2] sets energy policies for 2030, with buildings playing a crucial role in Europe’s transition to clean energy. Cutting energy demand and boosting efficiency in buildings [3] and industries [4] through energy-saving programs is an effective way to mitigate the impact of fossil-based resources. The “energy efficiency first” principle [5] calls for taking the utmost account of cost-efficient energy efficiency measures in shaping energy policy and making relevant investment decisions. The Renewable Energy Directive [6] and Energy Performance of Buildings Directive (EPBD) [7] promote, among other aspects, on-site renewable energy generation and self-consumption in EU countries.
The motivation behind the advancement of green technologies is the desire to diminish the environmental consequences and alleviate the increasing costs associated with electricity. In this context, photovoltaic (PV) systems are the most popular solutions among the most favored technologies due to their ability to generate small-scale electricity and easy integration into buildings [8]. End-users equipped with PV systems connected to the grid can generate electricity for self-consumption and sell the generation surplus to other buildings or the grid [9]. Energy distribution losses are reduced by generating electricity at the point of use [10] and the advances in technology have decreased the cost of PV panels, making them more attractive for building use [11]. However, integrating solar energy into buildings is challenging due to its variability [12]. Solar energy is only available during the daytime and is affected by climate, location, season, and time [13]. Therefore, the increasing integration of variable and intermittent renewables can cause a mismatch between supply and demand, disrupting power system stability, efficiency, quality, and reliability [14][15][16].

2. The Evolution of Energy in Buildings

To better understand the efforts to reduce energy consumption in buildings, a brief overview of the evolution of energy in buildings is presented in Figure 1. The building sector has undergone a significant transformation, starting with a passive approach to reducing energy consumption using passive solutions [17]. Then, nearly zero-energy buildings (nZEBs) were developed to balance energy demand and renewable generation through on-site RES production [18]. Later, the concept of energy-flexible buildings was introduced by the IEA EBC Annex 67 [19], enabling buildings to manage energy generation and demand based on factors such as weather conditions, user needs, and grid requirements. The latest phase of this evolution is smart buildings, which participate in the energy infrastructure, acting as both energy sellers and buyers [20].
Figure 1. The evolution of buildings over time.
Integrating buildings into communities through the exchange of renewable generation surplus is a suitable solution to tackle the technical and economic challenges of energy management. The concept of a renewable energy community is defined in both the “Directive on the Promotion of the Use of Energy from Renewable Sources” [6] and the “Directive on Common Rules for the Internal Electricity Market” [21]. It must be emphasized that a building’s energy demand is variable, and the worst case is when several customers’ peak consumption occurs simultaneously, especially if this occurs during a period of low renewable energy generation availability. This issue is a serious challenge to the balance of renewable supply and demand. Energy systems require flexibility to align with the varying energy demand over time, a necessity particularly emphasized in electric energy systems, where demand and supply must be matched at every moment [22]. Therefore, energy storage systems (ESS) and demand response (DR) play crucial roles in providing the needed flexibility to ensure the matching between renewable generation and demand [23][24]. The integration of PV and ESS systems into a community of buildings helps to ensure that end-users can use energy locally produced according to their needs while minimizing the negative impacts on the reliability of the grid [25]. The cost of static batteries has been high in the past decades, which is one of the reasons for batteries not being already widely used in the building sector. Fortunately, the cost of batteries has been decreasing due to technological advancements and is expected to trend downwards [26].
Moreover, electric vehicles (EVs) complement static batteries with their flexibility. With vehicle-to-building (V2B) systems, EV batteries’ excess capacity can supply energy to buildings [27]. EV batteries traditionally charge off-peak, or when energy is being produced locally, and provide energy during peak periods or later in the day when no local generation is available [28][29][30]. As relevant demand-side consumers, building communities can apply DR to optimize local generation integration and utilize energy storage systems [31]. In this context, shiftable loads also play a critical role in DR programs by allowing them to reduce peak demand [32]. Shiftable loads refer to electrical devices or appliances that can be scheduled to operate during periods of lower energy demand or when energy is more abundant and less expensive. Several loads in buildings are flexible, and their usage periods or cycles can be changed without affecting the comfort required by the occupants [33]. DR can be implemented via time-based and incentive-based programs [34].
In addition, building energy management systems (BEMS) play a critical role concerning energy management in the building sector and can be used to monitor and control energy demand [35]. Advanced energy management technologies, like BEMS, improve reliability and lower energy costs in buildings. BEMS can be used to optimize the matching between local energy generation and consumption and to reduce costs without sacrificing residents’ comfort in smart buildings [36][37]. In a literature review, Shareef et al. [38] analyzed the use of artificial intelligence (AI)-based controllers, such as artificial neural networks (ANN), fuzzy logic control, and adaptive neural fuzzy inference systems, in home energy management systems (HEMS) based on DR and intelligent controllers, discussing the strengths and weaknesses of each. Addressing the complexity of data is a common challenge for BEMS to ensure effective functionality. However, using the Internet of Energy (IoE) for the transfer of energy data at the required time and place, with applications in electricity distribution, network monitoring, communication, and ESS, can significantly reduce these issues [39][40].
Additionally, the Internet of Things (IoT), which connects new sensing and communication technologies to anything from anywhere at any time, is widely used in intelligent buildings [41][42][43] and can have a significant impact on reducing energy consumption when adequately integrated into BEMS systems [44]. In addition, the smart readiness indicator (SRI), proposed to rate buildings based on their ability to adapt operations to residents’ requirements, optimize energy efficiency, ensure overall performance, and respond appropriately to grid signals [45] plays a vital role in this context [46][47]. According to the Energy Performance of Buildings Directive (EPBD), buildings with a high SRI actively contribute to an intelligent energy system [48].

3. Energy Optimization in Buildings

Energy optimization plays a critical role in the building sector, being the main goal to minimize the energy consumption and energy costs of buildings while still providing comfort for the occupants [49]. Nowadays, energy optimization can have complex objectives, and the use of decision-making models and tools plays a crucial role. Operational research (OR) models and methods, such as multicriteria analysis (MCA), used to analyze possible alternatives and preferences and evaluate them under different criteria, and multi-objective optimization (MOO), which deals with optimizing solutions that satisfy multiple objectives, are effective in the energy sector for decision-making [50][51]. A system that considers technical, environmental, and economic factors is necessary for energy management, and the decisions should encompass sometimes conflicting objectives [52][53]. The importance of considering energy management is highlighted by A. Kumar et al. [54], and decision-making is critical when decisions have to be made based on several contradictory indicators [55].
In such a context, MCA involves evaluating multiple objectives and criteria to determine preferences among options in decision-making [56]. Despite MCA’s strengths in structuring and framing complex issues, it has some weaknesses in achieving optimal decisions and solutions [57]. For instance, in numerous applications of MCA, the selection of objectives and criteria often neglects proper consideration of the geographical and temporal aspects of the analysis [58]. Furthermore, MCA-based methods do not provide the designer with information on how sensitive each criterion is to changes in the other criteria. The literature shows a gap between theory and practice in MCA’s application [57]. MOO is important because it can model real-world problems with multiple conflicting objectives [59][60]. To address the various challenges in the building energy sector, energy optimization applying MOO is essential to meet users’ needs while also reducing technical issues and energy usage in the building energy sector.

References

  1. Li, Y.; Zhu, N.; Qin, B. What Affects the Progress and Transformation of New Residential Building Energy Efficiency Promotion in China: Stakeholders’ Perceptions. Energies 2019, 12, 1027.
  2. Capros, P.; Kannavou, M.; Evangelopoulou, S.; Petropoulos, A.; Siskos, P.; Tasios, N.; Zazias, G.; DeVita, A. Outlook of the EU Energy System up to 2050: The Case of Scenarios Prepared for European Commission’s “Clean Energy for All Europeans” Package Using the PRIMES Model. Energy Strategy Rev. 2018, 22, 255–263.
  3. Belussi, L.; Barozzi, B.; Bellazzi, A.; Danza, L.; Devitofrancesco, A.; Fanciulli, C.; Ghellere, M.; Guazzi, G.; Meroni, I.; Salamone, F.; et al. A review of performance of zero energy buildings and energy efficiency solutions. J. Build. Eng. 2019, 25, 100772.
  4. Chen, H.; Shi, Y.; Zhao, X. Investment in renewable energy resources, sustainable financial inclusion and energy efficiency: A case of US economy. Resour. Policy 2022, 77, 102680.
  5. Mandel, T.; Pató, Z.; Broc, J.-S.; Eichhammer, W. Conceptualising the energy efficiency first principle: Insights from theory and practice. Energy Effic. 2022, 15, 41.
  6. European Parliamentary Research Service (EPRS). DIRECTIVE (EU) 2018/2001 of the European Parliament and of the Council of 11 December 2018 on the Promotion of the Use of Energy from Renewable Sources, Official Journal of the European Union. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32018L2001 (accessed on 30 July 2023).
  7. The European Parliament and The Council of the European Union. Directive 2010/31/EU of the European Parliament and of the Council of 19 May 2010 on the Energy Performance of Buildings (Recast), Official Journal of the European Union. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32010L0031 (accessed on 30 July 2023).
  8. Chomać-Pierzecka, E.; Rogozińska-Mitrut, J.; Różycka, M.; Soboń, D.; Stasiak, J. Energy Innovation for Individual Consumers in Poland—Analysis of Potential and Evaluation of Practical Applications in Selected Areas. Energies 2023, 16, 5766.
  9. Li, Y.; Gao, W.; Ruan, Y. Performance investigation of grid-connected residential PV-battery system focusing on enhancing self-consumption and peak shaving in Kyushu, Japan. Renew. Energy 2018, 127, 514–523.
  10. Koskela, J.; Rautiainen, A.; Järventausta, P. Using electrical energy storage in residential buildings—Sizing of battery and photovoltaic panels based on electricity cost optimization. Appl. Energy 2019, 239, 1175–1189.
  11. Kavlak, G.; McNerney, J.; Trancik, J.E. Evaluating the causes of cost reduction in photovoltaic modules. Energy Policy 2018, 123, 700–710.
  12. Nwaigwe, K.; Mutabilwa, P.; Dintwa, E. An overview of solar power (PV systems) integration into electricity grids. Mater. Sci. Energy Technol. 2019, 2, 629–633.
  13. Ahmed, R.; Sreeram, V.; Mishra, Y.; Arif, M.D. A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization. Renew. Sustain. Energy Rev. 2020, 124, 109792.
  14. Kaushik, E.; Prakash, V.; Mahela, O.P.; Khan, B.; El-Shahat, A.; Abdelaziz, A.Y. Comprehensive Overview of Power System Flexibility during the Scenario of High Penetration of Renewable Energy in Utility Grid. Energies 2022, 15, 516.
  15. Ranjan, M.; Shankar, R. A literature survey on load frequency control considering renewable energy integration in power system: Recent trends and future prospects. J. Energy Storage 2022, 45, 103717.
  16. Basit, M.A.; Dilshad, S.; Badar, R.; Rehman, S.M.S.U. Limitations, challenges, and solution approaches in grid-connected renewable energy systems. Int. J. Energy Res. 2020, 44, 4132–4162.
  17. Vigna, I.; Pernetti, R.; Pasut, W.; Lollini, R. New domain for promoting energy efficiency: Energy Flexible Building Cluster. Sustain. Cities Soc. 2018, 38, 526–533.
  18. Paoletti, G.; Pascuas, R.P.; Pernetti, R.; Lollini, R. Nearly Zero Energy Buildings: An Overview of the Main Construction Features across Europe. Buildings 2017, 7, 43.
  19. 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 and Buildings 2017, 155, 25–34.
  20. Hakimi, S.M.; Hasankhani, A. Intelligent energy management in off-grid smart buildings with energy interaction. J. Clean. Prod. 2019, 244, 118906.
  21. The European Parliament and the Council of the European Union. DIRECTIVE (EU) 2019/944 of the European Parliament and of the Council of 5 June 2019 on Common Rules for the Internal Market for Electricity and Amending Directive 2012/27/EU (Recast), Official Journal of the European Union. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32019L0944 (accessed on 30 July 2023).
  22. Leonard, M.D.; Michaelides, E.E.; Michaelides, D.N. Energy storage needs for the substitution of fossil fuel power plants with renewables. Renew. Energy 2020, 145, 951–962.
  23. Barreto, R.; Gonçalves, C.; Gomes, L.; Faria, P.; Vale, Z. Evaluation Metrics to Assess the Most Suitable Energy Community End-Users to Participate in Demand Response. Energies 2022, 15, 2380.
  24. Bintoudi, A.D.; Bezas, N.; Zyglakis, L.; Isaioglou, G.; Timplalexis, C.; Gkaidatzis, P.; Tryferidis, A.; Ioannidis, D.; Tzovaras, D. Incentive-Based Demand Response Framework for Residential Applications: Design and Real-Life Demonstration. Energies 2021, 14, 4315.
  25. Ranaweera, I.; Midtgård, O.-M. Optimization of operational cost for a grid-supporting PV system with battery storage. Renew. Energy 2016, 88, 262–272.
  26. Conway, G.; Joshi, A.; Leach, F.; García, A.; Senecal, P.K. A review of current and future powertrain technologies and trends in 2020. Transp. Eng. 2021, 5, 100080.
  27. He, Z.; Khazaei, J.; Freihaut, J.D. Optimal Integration of Vehicle to Building (V2B) and Building to Vehicle (B2V) Technologies for Commercial Buildings. Sustain. Energy Grids Netw. 2022, 32, 100921.
  28. Lokesh, B.T.; Hui Min, J.T. A Framework for Electric Vehicle (EV) Charging in Singapore. Energy Procedia 2017, 143, 15–20.
  29. Casals, L.C.; Amante García, B.; Canal, C. Second life batteries lifespan: Rest of useful life and environmental analysis. J. Environ. Manag. 2019, 232, 354–363.
  30. Hassan, A.S.; Cipcigan, L.; Jenkins, N. Optimal battery storage operation for PV systems with tariff incentives. Appl. Energy 2017, 203, 422–441.
  31. Rasouli, V.; Gomes, V.; Antunes, C.H. An optimization model to characterize the aggregated flexibility responsiveness of residential end-users. Int. J. Electr. Power Energy Syst. 2023, 144, 108563.
  32. Cui, Q.; Zhu, J.; Chen, J.; Ma, Z.; Shu, J. Optimal operation of CCHP microgrids with multiple shiftable loads in different auxiliary heating source systems. Energy Rep. 2022, 8, 628–638.
  33. Klein, K.; Herkel, S.; Henning, H.-M.; Felsmann, C. Load shifting using the heating and cooling system of an office building: Quantitative potential evaluation for different flexibility and storage options. Appl. Energy 2017, 203, 917–937.
  34. Aghamohamadi, M.; Hajiabadi, M.E.; Samadi, M. A novel approach to multi energy system operation in response to DR programs; an application to incentive-based and time-based schemes. Energy 2018, 156, 534–547.
  35. Martirano, L.; Parise, G.; Greco, G.; Manganelli, M.; Massarella, F.; Cianfrini, M.; Parise, L.; Frattura, P.d.L.; Habib, E. Aggregation of Users in a Residential/Commercial Building Managed by a Building Energy Management System (BEMS). IEEE Trans. Ind. Appl. 2018, 55, 26–34.
  36. Nizami, M.; Hossain, M.; Amin, B.R.; Fernandez, E. A residential energy management system with bi-level optimization-based bidding strategy for day-ahead bi-directional electricity trading. Appl. Energy 2019, 261, 114322.
  37. Manic, M.; Wijayasekara, D.; Amarasinghe, K.; Rodriguez-Andina, J.J. Building Energy Management Systems: The Age of Intelligent and Adaptive Buildings. IEEE Ind. Electron. Mag. 2016, 10, 25–39.
  38. Shareef, H.; Ahmed, M.S.; Mohamed, A.; Al Hassan, E. Review on Home Energy Management System Considering Demand Responses, Smart Technologies, and Intelligent Controllers. IEEE Access 2018, 6, 24498–24509.
  39. Hannan, M.A.; Faisal, M.; Ker, P.J.; Mun, L.H.; Parvin, K.; Mahlia, T.M.I.; Blaabjerg, F. A Review of Internet of Energy Based Building Energy Management Systems: Issues and Recommendations. IEEE Access 2018, 6, 38997–39014.
  40. Miglani, A.; Kumar, N.; Chamola, V.; Zeadally, S. Blockchain for Internet of Energy management: Review, solutions, and challenges. Comput. Commun. 2020, 151, 395–418.
  41. Hossain, M.; Weng, Z.; Schiano-Phan, R.; Scott, D.; Lau, B. Application of IoT and BEMS to Visualise the Environmental Performance of an Educational Building. Energies 2020, 13, 4009.
  42. Jia, M.; Komeily, A.; Wang, Y.; Srinivasan, R.S. Adopting Internet of Things for the development of smart buildings: A review of enabling technologies and applications. Autom. Constr. 2019, 101, 111–126.
  43. Nguyen, V.T.; Luan Vu, T.; Le, N.T.; Min Jang, Y. An Overview of Internet of Energy (IoE) Based Building Energy Management System. In Proceedings of the 2018 International Conference on Information and Communication Technology Convergence (ICTC), Jeju, Republic of Korea, 17–19 October 2018; pp. 852–855.
  44. Naqbi, A.A.; Alyieliely, S.S.; Talib, M.A.; Nasir, Q.; Bettayeb, M.; Ghenai, C. Energy Reduction in Building Energy Management Systems Using the Internet of Things: Systematic Literature Review. In Proceedings of the 2021 International Symposium on Networks, Computers and Communications (ISNCC), Dubai, United Arab Emirates, 31 October–2 November 2021; pp. 1–7.
  45. Märzinger, T.; Österreicher, D. Supporting the Smart Readiness Indicator—A Methodology to Integrate A Quantitative Assessment of the Load Shifting Potential of Smart Buildings. Energies 2019, 12, 1955.
  46. Al-Obaidi, K.M.; Hossain, M.; Alduais, N.A.M.; Al-Duais, H.S.; Omrany, H.; Ghaffarianhoseini, A. A Review of Using IoT for Energy Efficient Buildings and Cities: A Built Environment Perspective. Energies 2022, 15, 5991.
  47. Ożadowicz, A. A Hybrid Approach in Design of Building Energy Management System with Smart Readiness Indicator and Building as a Service Concept. Energies 2022, 15, 1432.
  48. The European Parliament and the Council of the European Union. Directive (EU) 2018/488 of the European Parliament and of the Council of 30 May 2018 Amending Directive 2010/31/EU on the Energy Performance of Buildings and Directive 2012/27/EU on Energy Efficiency (Text with EEA Relevance), Official Journal of the European Union. Available online: https://eur-lex.europa.eu/legal-content/EN/TXT/HTML/?uri=CELEX:32018L0844 (accessed on 30 July 2023).
  49. Chenari, B.; Lamas, F.B.; Gaspar, A.R.; da Silva, M.G. Simulation of Occupancy and CO 2 -based Demand-controlled Mechanical Ventilation Strategies in an Office Room Using EnergyPlus. Energy Procedia 2017, 113, 51–57.
  50. Asadi, E.; da Silva, M.G.; Antunes, C.H.; Dias, L. Multi-objective optimization for building retrofit strategies: A model and an application. Energy Build. 2012, 44, 81–87.
  51. Asadi, E.; da Silva, M.G.; Antunes, C.H.; Dias, L. A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB. Build. Environ. 2012, 56, 370–378.
  52. Nemati, M.; Braun, M.; Tenbohlen, S. Optimization of unit commitment and economic dispatch in microgrids based on genetic algorithm and mixed integer linear programming. Appl. Energy 2018, 210, 944–963.
  53. Brans, J.-P.; De Smet, Y. PROMETHEE Methods. In Multiple Criteria Decision Analysis: State of the Art Surveys; Greco, S., Ehrgott, M., Figueira, J.R., Eds.; International Series in Operations Research & Management Science; Springer: New York, NY, USA, 2016; pp. 187–219. ISBN 978-1-4939-3094-4.
  54. Kumar, A.; Sah, B.; Singh, A.R.; Deng, Y.; He, X.; Kumar, P.; Bansal, R.C. A review of multi criteria decision making (MCDM) towards sustainable renewable energy development. Renew Sustain. Energy Rev. 2017, 69, 596–609.
  55. Rigo, P.D.; Rediske, G.; Rosa, C.B.; Gastaldo, N.G.; Michels, L.; Júnior, A.L.N.; Siluk, J.C.M. Renewable Energy Problems: Exploring the Methods to Support the Decision-Making Process. Sustainability 2020, 12, 10195.
  56. Yannis, G.; Kopsacheili, A.; Dragomanovits, A.; Petraki, V. State-of-the-art review on multi-criteria decision-making in the transport sector. J. Traffic Transp. Eng. 2020, 7, 413–431.
  57. Dean, M. Chapter Six—Multi-Criteria Analysis. In Advances in Transport Policy and Planning; Mouter, N., Ed.; Standard Transport Appraisal Methods; Academic Press: Cambridge, MA, USA, 2020; Volume 6, pp. 165–224.
  58. Dean, M. Multi-Criteria Analysis. In Advances in Transport Policy and Planning; Elsevier: Amsterdam, The Netherlands, 2020; Volume 6, pp. 165–224. ISBN 978-0-12-820821-2.
  59. Karimi, N.; Feylizadeh, M.R.; Govindan, K.; Bagherpour, M. Fuzzy multi-objective programming: A systematic literature review. Expert Syst. Appl. 2022, 196, 116663.
  60. Hasson, S.T.; Ayad Khudhair, H. Developed NSGA-II to Solve Multi Objective Optimization Models in WSNs. In Proceedings of the 2018 International Conference on Advanced Science and Engineering (ICOASE), Duhok, Iraq, 9–11 October 2018; pp. 19–23.
More
© Text is available under the terms and conditions of the Creative Commons Attribution (CC BY) license; additional terms may apply. By using this site, you agree to the Terms and Conditions and Privacy Policy.
Upload a video for this entry
Information
Subjects: Engineering, Electrical & Electronic
Contributors MDPI registered users' name will be linked to their SciProfiles pages. To register with us, please refer to https://encyclopedia.pub/register :
Omid Pedram
,
Ehsan Asadi
,
Behrang Chenari
,
Pedro Moura
,
Manuel Gameiro da Silva
View Times: 243
Revisions: 2 times (View History)
Update Date: 12 Sep 2023
Table of Contents
    1000/1000
    Hot Most Recent
    ScholarVision Creations
    Feedback