An Embedded Platform for Testbed Implementation of Multi-Agent System in Building Energy Management System
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This paper presents a hardware testbed for testing the building energy management system (BEMS) based-on the multi agent system (MAS). The objective of BEMS is to maximize user comfort while minimizing the energy extracted from the grid. The proposed system implements a multi-objective optimization technique using a genetic algorithm (GA) and the fuzzy logic controller (FLC) to control the room temperature and illumination setpoints. The agents are implemented on the low cost embedded systems equipped with the WiFi communication for communicating between the agents. The photovoltaic (PV)-battery system, the air conditioning system, the lighting system, and the electrical loads are modeled and simulated on the embedded hardware. The popular communication protocols such as Message Queuing Telemetry Transport (MQTT) and Modbus TCP/IP are adopted for integrating the proposed MAS with the existing infrastructures and devices. The experimental results show that the sampling time of the proposed system is 16.50 s. Therefore it is suitable for implementing the BEMS in a real-time where the data are updated in an hourly or minutely basis. Further, the proposed optimization technique shows better results in optimizing the comfort index and the energy extracted from the grid compared to the existing methods.
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The adoption of the Modbus protocol provides a wide range of implementation options without replacing existing sensor and actuator devices.
“As asserted in [47], the adoption of Modbus protocol provides a wide range of implementation options without replacing the existing sensor and actuator devices.”
From Innovative Multi-Layered Architecture for Heterogeneous Automation and Monitoring Systems: Application Case of a Photovoltaic Smart Microgrid (2021) · cites this paper as [47]
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Cites
A fast and elitist multiobjective genetic algorithm: NSGA-II
Deb, K., Pratap, A., Agarwal, S., & Meyarivan, T. (2002) · IEEE Transactions on Evolutionary Computation · doi:10.1109/4235.996017 · Cited by 50,053 · see citing works
Factorial Sampling Plans for Preliminary Computational Experiments
Morris, M. D. (1991) · Technometrics · doi:10.1080/00401706.1991.10484804 · Cited by 4,943 · see citing works
Geidl, M., Koeppel, G., Favre‐Perrod, P., et al. (2007) · IEEE Power and Energy Magazine · doi:10.1109/mpae.2007.264850 · Cited by 1,142 · see citing works
Study of the sensitivity of coupled reaction systems to uncertainties in rate coefficients. I Theory
Cukier, R. I., Fortuin, C. M., Shuler, K. E., Petschek, A. G., & Schaibly, J. H. (1973) · The Journal of Chemical Physics · doi:10.1063/1.1680571 · Cited by 1,046 · see citing works
A Matlab toolbox for Global Sensitivity Analysis
Pianosi, F., Sarrazin, F., & Wagener, T. (2015) · Environmental Modelling & Software · doi:10.1016/j.envsoft.2015.04.009 · Cited by 656 · see citing works
Energy management and planning in smart cities
Calvillo, C., Miralles, Á. S., & Villar, J. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.10.133 · Cited by 483 · see citing works
A multi-agent based energy management solution for integrated buildings and microgrid system
Anvari-Moghaddam, A., Rahimi-Kian, A., Mirian, M., & Guerrero, J. (2017) · Applied Energy · doi:10.1016/j.apenergy.2017.06.007 · Cited by 267 · see citing works
Esther, B. P., & Kumar, K. S. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.282 · Cited by 264 · see citing works
Zhao, P. B., Suryanarayanan, S., & Simões, M. G. (2012) · IEEE Transactions on Industry Applications · doi:10.1109/tia.2012.2229682 · Cited by 244 · see citing works
Optimization Models and Methods for Demand-Side Management of Residential Users: A Survey
Barbato, A., & Capone, A. (2014) · Energies · doi:10.3390/en7095787 · Cited by 205 · see citing works
Multi-agent control system with information fusion based comfort model for smart buildings
Wang, Z., Wang, L., Dounis, A. I., et al. (2012) · Applied Energy · doi:10.1016/j.apenergy.2012.05.020 · Cited by 185 · see citing works
A SCADA system for energy management in intelligent buildings
Figueiredo, J. M. G., & da Costa, J. S. (2012) · Energy and Buildings · doi:10.1016/j.enbuild.2012.01.041 · Cited by 134 · see citing works
Energy Management System for an Energy District With Demand Response Availability
Brusco, G., Burgio, A., Menniti, D., et al. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2014.2318894 · Cited by 104 · see citing works
Carli, R., & Dotoli, M. (2019) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2019.1911462 · Cited by 95 · see citing works
Integrated simulation of power and communication networks for smart grid applications
Mets, K., Verschueren, T., Develder, C., et al. (2011) · IEEE International Workshop on Computer Aided Modeling and Design of Communication Links and Networks (CAMAD) · doi:10.1109/camad.2011.5941119 · Cited by 89 · see citing works
A Hierarchical Decision-Making Strategy for the Energy Management of Smart Cities
Carli, R., Dotoli, M., & Pellegrino, R. (2016) · IEEE Transactions on Automation Science and Engineering · doi:10.1109/tase.2016.2593101 · Cited by 76 · see citing works
Intelligent multi-agent system for smart home energy management
Li, W., Logenthiran, T., & Woo, W. L. (2015) · IEEE Innovative Smart Grid Technologies-Asia · doi:10.1109/isgt-asia.2015.7386985 · Cited by 70 · see citing works
Adaptive On-line State-of-available-power Prediction of Lithium-ion Batteries
Fleischer, C., Waag, W., Bai, Z., & Sauer, D. U. (2013) · Journal of Power Electronics · doi:10.6113/jpe.2013.13.4.516 · Cited by 68 · see citing works
Intelligent multi-objective optimization for building energy and comfort management
Shaikh, P. H., Nor, N. M., Nallagownden, P., et al. (2016) · Journal of King Saud University - Engineering Sciences · doi:10.1016/j.jksues.2016.03.001 · Cited by 66 · see citing works
Graditi, G., Ippolito, M. G., Lamedica, R., et al. (2015) · Energy and Buildings · doi:10.1016/j.enbuild.2015.05.027 · Cited by 62 · see citing works
Smart Grid Testbed for Demand Focused Energy Management in End User Environments
Tushar, W., Yuen, C., Chai, B., et al. (2016) · IEEE Wireless Communications · doi:10.1109/mwc.2016.1400377rp · Cited by 62 · see citing works
Multi-agent system architecture for smart home energy management and optimization
Asare-Bediako, B., Kling, W. L., & Ribeiro, P. F. (2013) · doi:10.1109/isgteurope.2013.6695331 · Cited by 60 · see citing works
Ullah, I., & Kim, D. (2017) · Energies · doi:10.3390/en10111818 · Cited by 60 · see citing works
Multi-agent intelligent controller design for smart and sustainable buildings
Wang, Z., Yang, R., & Wang, L. (2010) · 2020 IEEE International Systems Conference (SysCon) · doi:10.1109/systems.2010.5482326 · Cited by 55 · see citing works
Decentralized Resource Allocation and Load Scheduling for Multicommodity Smart Energy Systems
Blaauwbroek, N., Nguyen, P. H., Konsman, M. J., Shi, H., Kamphuis, R. I. G., & Kling, W. L. (2015) · IEEE Transactions on Sustainable Energy · doi:10.1109/tste.2015.2441107 · Cited by 55 · see citing works
Predictive models for building's energy consumption: An Artificial Neural Network (ANN) approach
Ferlito, S., Atrigna, M., Graditi, G., et al. (2015) · doi:10.1109/aisem.2015.7066836 · Cited by 52 · see citing works
Sánchez‐Squella, A., Ortega, R. S., Griñó, R., et al. (2010) · IEEE Control Systems · doi:10.1109/mcs.2010.938096 · Cited by 47 · see citing works
Building Energy Management Systems — Optimization of comfort and energy use
Hurtado, L. A., Nguyen, P. H., Kling, W. L., & Zeiler, W. (2013) · International Universities Power Engineering Conference (UPEC) · doi:10.1109/upec.2013.6714910 · Cited by 45 · see citing works
Energy scheduling of a smart home under nonlinear pricing
Carli, R., & Dotoli, M. (2014) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2014.7040273 · Cited by 42 · see citing works
Implementation of a building energy management system for residential demand response
Rotger-Griful, S., Welling, U., & Jacobsen, R. H. (2017) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2017.10.006 · Cited by 41 · see citing works
Multi-objective optimal analysis of comfort and energy management for intelligent buildings
Wang, N., Fang, F., & Feng, M. (2014) · doi:10.1109/ccdc.2014.6852646 · Cited by 39 · see citing works
Carli, R., & Dotoli, M. (2015) · Proceedings of the IEEE Conference on Decision & Control, including the Symposium on Adaptive Processes · doi:10.1109/cdc.2015.7403147 · Cited by 36 · see citing works
Carli, R., & Dotoli, M. (2017) · IFAC-PapersOnLine · doi:10.1016/j.ifacol.2017.08.1544 · Cited by 36 · see citing works
A distributed power sharing framework among households in microgrids: a repeated game approach
AlSkaif, T. A., Zapata, M. G., Bellalta, B., & Nilsson, A. (2016) · Computing · doi:10.1007/s00607-016-0504-y · Cited by 33 · see citing works
A Fuzzy-Based Building Energy Management System for Energy Efficiency
Hernández, J. L., Sanz, R., Corredera, Á., et al. (2018) · Buildings · doi:10.3390/buildings8020014 · Cited by 33 · see citing works
Multi-agent system for energy consumption optimisation in higher education institutions
Al-Daraiseh, A. A., El‐Qawasmeh, E., & Shah, N. (2014) · Journal of Computer and System Sciences · doi:10.1016/j.jcss.2014.12.010 · Cited by 29 · see citing works
An Agent Based Fuzzy Control for Smart Home Energy Management in Smart Grid Environment
(2017) · International Journal of Renewable Energy Research · doi:10.20508/ijrer.v7i2.5475.g7035 · Cited by 28 · see citing works
Distributed multi-agent algorithm for residential energy management in smart grids
Mets, K., Strobbe, M., Verschueren, T., Roelens, T., Turck, F. D., & Develder, C. (2012) · IEEE/IFIP Network Operations and Management Symposium (NOMS) · doi:10.1109/noms.2012.6211928 · Cited by 25 · see citing works
An Integrated Scheme for Cyber-physical Building Energy Management System
Wang, S., Zhang, G., Shen, B., et al. (2011) · Procedia Engineering · doi:10.1016/j.proeng.2011.08.677 · Cited by 23 · see citing works
Intelligent energy management in smart and sustainable buildings with multi-agent control system
Smitha, S. D., & Chacko, F. M. (2013) · International Mutli-Conference on Automation, Computing, Communication, Control and Compressed Sensing · doi:10.1109/imac4s.2013.6526406 · Cited by 17 · see citing works
Energy Management With a World-Wide Adaptive Thermostat Using Fuzzy Inference System
Javaid, S., Javaid, N., Iqbal, S., Guizani, M., Almogren, A., & Alamri, A. M. (2018) · IEEE Access · doi:10.1109/access.2018.2828040 · Cited by 16 · see citing works
A Multi-agent Coordination Framework for Smart Building Energy Management
Stavropoulos, T. G., Rigas, E. S., Kontopoulos, E., et al. (2014) · Proceedings - International Workshop on Database and Expert Systems Applications/Proceedings · doi:10.1109/dexa.2014.39 · Cited by 12 · see citing works
Optimized intelligent control system for indoor thermal comfort and energy management of buildings
Shaikh, P. H., Nor, N. M., Nallagownden, P., et al. (2014) · International Conference on Intelligent and Advanced Systems · doi:10.1109/icias.2014.6869454 · Cited by 11 · see citing works
Sharma, S., Panigrahi, B. K., & Verma, A. (2018) · IEEE Consumer Electronics Magazine · doi:10.1109/mce.2017.2713646 · Cited by 8 · see citing works
Energy management by controlling air conditioning systems in residential settings
Gubba, S. R., Li, W.-T., Tushar, W., et al. (2016) · IEEE Workshop on Environmental, Energy, and Structural Monitoring Systems (EESMS) · doi:10.1109/eesms.2016.7504808 · Cited by 7 · see citing works
Multi-agent control system with intelligent optimisation for building energy and comfort management
Kan, E. M., Kan, S. L., Soh, Y., et al. (2016) · International Journal of Automation and Logistics · doi:10.1504/ijal.2016.074914 · Cited by 5 · see citing works
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Research works printed but not linked: 3
- Fourer, R.; Gay, D.M.; Kernighan, B.W. AMPL-A Modeling Language for Mathematical Programming, 2nd ed.; Duxbury Press/Brooks/Cole Publishing Company: Pacific Grove, CA, USA, 2003.
- Naito, K.; Mori, K.; Kobayashi, H. Testbed implementation of cloud based energy management system with ZigBee sensor networks. In Proceedings of the International Multi-Conference on Engineering and Technological Innovation, Orlando, FL, USA, 9-13 July 2013.
- Thermal Model of a House. Available online: https://fr.mathworks.com/help/simulink/slref/thermal-model- of-a-house.html (accessed on 27 July 2019). Energies 2019, 12, 3655 29 of 29
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Sorted by citations
González, I., Calderón, A. J., & Portalo, J. M. (2021) · Sustainability · doi:10.3390/su13042234 · Cited by 90 · see citing works
- Authors
- Soetedjo, A., Nakhoda, Y. I., & Saleh, C.
- Affiliations
- Institut Teknologi Nasional Malang (ID)
- Corresponding author
- Soetedjo, Aryuanto
- Venue
- Energies
- Publisher
- MDPI AG
- Published
- 2019-09-25
- Volume
- 12
- Issue
- 19
- Pages
- 3655
- ISSN
- 1996-1073 (ISSN-L)
- OpenAlex
- W2976322392
- Cited by
- 11
- References
- 46
- Citations by year
- 2025: 1 · 2024: 2 · 2022: 3 · 2021: 4 · 2020: 1
- FWCI
- 1.34
- Type
- journal article
- Language
- en
- Open access
- gold
- dx.doi.org
- Licence
- Locations
- Energies (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Directorate General of Higher Education, Ministry of Research and Technology and Higher 659 Education, Republic of Indonesia (079/SP2H/LT/MONO/L7/2019)
- Topics
- Physical Sciences › Engineering › Building and Construction › Building Energy and Comfort Optimization
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
- Physical Sciences › Engineering › Electrical and Electronic Engineering › IoT-based Smart Home Systems
- Keywords
- building energy management systems, multi-agent systems, genetic algorithm, fuzzy logic controller, multi-objective optimization, room temperature control, photovoltaic-battery systems, air conditioning, MQTT, Modbus TCP, user comfort, grid energy consumption
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