Proposal for an intelligent control system for energy saving in a home automation environment.
DOI:
https://doi.org/10.32870/bxfwa097Keywords:
Artificial neural netwoks, home automation, Smart network, Intelligent controlAbstract
Smart grids and their growing application to establish intelligent systems for monitoring energy consumption require the incorporation of proposals in the field of control and electronics. One of the global trends in energy is the use of hybrid generation systems in homes. These consist of the combination of traditional electricity consumption, shared with production models in homes. This entails the need to generate decision-making systems that allow the optimal and real-time combination of owned and commercial energy generation systems. In this way, an intelligent control system was created that allows two energy sources to be combined: a commercial and a domestic one. The objective is to exchange the stored electricity for electricity from the electricity grid in a home automation environment depending on the environmental and consumption conditions. To this end, a database was created consisting of four attributes: current consumption, percentage of load of the storage system of the non-commercial system, presence of sunlight, wind speed, feedback of the status of the previous output. In the output you have a class with two values: non-commercial system connection and commercial network connection. Different models of decision-making tools were evaluated, and it was obtained that the best performance in the tests was given by an Artificial Neural Network trained through supervised learning. The result is an intelligent system that allows the power supplies of a house to be intelligently switched and that is typical of smart grids that were evaluated in a simulated environment and then implemented in a physical system.References
1. Alassi, A., Feng, Z., Ahmed, K., Syed, M., Egea-Alvarez, A., & Foote, C. (2023). Grid-forming VSM control for black-start applications with experimental PHiL validation. International Journal of Electrical Power & Energy Systems, 151, 109119. https://doi.org/10.1016/j.ijepes.2023.109119 ScienceDirect
2. Aldana-Franco, F., Aldana-Franco, R., Sánchez-Álvarez, E., & López-Velázquez, A. (2018). SORNA_CP: Una herramienta de optimización de redes neuronales artificiales para la ingeniería de control. En CIREMI 2018.
Nota: no localicé un DOI ni una fuente editorial suficientemente confiable para completar esta referencia. Conviene verificar las actas o memorias originales del congreso.
3. Banerjee, S., & Bhowmik, P. S. (2023). A machine learning approach based on decision tree algorithm for classification of transient events in microgrid. Electrical Engineering, 105(4), 2083–2093. https://doi.org/10.1007/s00202-023-01796-5 ResearchGate
4. Bhadani, U. (2024). Smart grids: A cyber–physical systems perspective. International Research Journal of Engineering and Technology (IRJET), 11(6), 801. https://www.irjet.net/archives/V11/i6/IRJET-V11I6117.pdf IRJET
5. Bharti, K., Singh, V. P., & Singh, S. P. (2022). Impact of intelligent demand response for load frequency control in smart grid perspective. IETE Journal of Research, 68(4), 2433–2444. https://doi.org/10.1080/03772063.2019.1709570 Tandfonline
6. Bolurian, A., Akbari, H., Mousavi, S., & Aslinezhad, M. (2023). Bi-level energy management model for the smart grid considering customer behavior in the wireless sensor network platform. Sustainable Cities and Society, 88, 104281. https://doi.org/10.1016/j.scs.2022.104281 ScienceDirect
7. Chacon Troya, D. P., Campoverde Campoverde, P. D., & Ortiz Gonzalez, O. O. (2018). Domotic application for the monitoring and control of residential electrical loads. En 2017 IEEE 37th Central America and Panama Convention (CONCAPAN XXXVII). IEEE. https://doi.org/10.1109/CONCAPAN.2017.8278471 Universidad Politécnica Salesiana
8. Dewangan, F., Abdelaziz, A. Y., & Biswal, M. (2023). Load forecasting models in smart grid using smart meter information: A review. Energies, 16(3), 1404. https://doi.org/10.3390/en16031404 MDPI
9. Fotopoulou, M., Rakopoulos, D., Petridis, S., & Drosatos, P. (2024). Assessment of smart grid operation under emergency situations. Energy, 287, 129661. https://doi.org/10.1016/j.energy.2023.129661 ScienceDirect
10. Gallegos, J., Arévalo, P., Montaleza, C., & Jurado, F. (2024). Sustainable electrification—Advances and challenges in electrical-distribution networks: A review. Sustainability, 16(2), 698. https://doi.org/10.3390/su16020698 MDPI
11. Guan, X., Xu, Z., & Jia, Q.-S. (2010). Energy-efficient buildings facilitated by microgrid. IEEE Transactions on Smart Grid, 1(3), 243–252. https://doi.org/10.1109/TSG.2010.2083705 Canyam
12. Hossain, N. U. I., & Shah, C. (2023). Dynamic Bayesian network based approach for modeling and assessing resilience of smart grid system. En M. Fathi, E. Zio, & P. M. Pardalos (Eds.), Handbook of smart energy systems (pp. 1613–1632). Springer. https://doi.org/10.1007/978-3-030-97940-9_16 IDEAS/RePEc
13. Hussaian Basha, C. H., Ramakrishna Reddy, K., Dhanamjayulu, C., Kamwa, I., & Muyeen, S. M. (2024). A novel on intelligent energy control strategy for micro grids with renewables and EVs. Energy Strategy Reviews, 52, 101306. https://doi.org/10.1016/j.esr.2024.101306 ScienceDirect
14. Jafari, M., Malekjamshidi, Z., Zhu, J., & Khooban, M. H. (2020). A novel predictive fuzzy logic-based energy management system for grid-connected and off-grid operation of residential smart microgrids. IEEE Journal of Emerging and Selected Topics in Power Electronics, 8(2), 1391–1404. https://doi.org/10.1109/JESTPE.2018.2882509 Aalborg Universitets forskningsportal
15. Khalid, M. (2024). Smart grids and renewable energy systems: Perspectives and grid integration challenges. Energy Strategy Reviews, 51, 101299. https://doi.org/10.1016/j.esr.2024.101299 DOI
16. Khan, A. A., Laghari, A. A., Rashid, M., Li, H., Javed, A. R., & Gadekallu, T. R. (2023). Artificial intelligence and blockchain technology for secure smart grid and power distribution automation: A state-of-the-art review. Sustainable Energy Technologies and Assessments, 57, 103282. https://doi.org/10.1016/j.seta.2023.103282 ScienceDirect
17. Lee, S., Seon, J., Sun, Y. G., Kim, S. H., Kyeong, C., Kim, D. I., & Kim, J. Y. (2024). Novel architecture of energy management systems based on deep reinforcement learning in microgrid. IEEE Transactions on Smart Grid, 15(2), 1646–1658. https://doi.org/10.1109/TSG.2023.3317096 Sungkyunkwan University
18. Llanez-Caballero, I., Ibarra, L., Peña-Quintal, A., Catzín-Contreras, G., Ponce, P., Molina, A., & Ramirez-Mendoza, R. (2023). The “smart” concept from an electrical sustainability viewpoint. Energies, 16(7), 3072. https://doi.org/10.3390/en16073072 MDPI
19. Mansouri, S. A., Ahmarinejad, A., Nematbakhsh, E., Javadi, M. S., Jordehi, A. R., & Catalão, J. P. S. (2021). Energy management in microgrids including smart homes: A multi-objective approach. Sustainable Cities and Society, 69, 102852. https://doi.org/10.1016/j.scs.2021.102852 ScienceDirect
20. Mohammadi, Y., Shakouri, H., & Kazemi, A. (2022). A multi-objective fuzzy optimization model for electricity generation and consumption management in a micro smart grid. Sustainable Cities and Society, 86, 104119. https://doi.org/10.1016/j.scs.2022.104119 ScienceDirect
21. Mohsen, S., Bajaj, M., Kotb, H., Pushkarna, M., Alphonse, S., & Ghoneim, S. S. M. (2023). Efficient artificial neural network for smart grid stability prediction. International Transactions on Electrical Energy Systems, 2023, 9974409. https://doi.org/10.1155/2023/9974409 Wiley Online Library
22. Ogundipe, O. L., Nwafor, V. U., Ajuwon, A. A., Uwagboe, E. U., Ajisegiri, E. E. S. A., Adediran, A. A., Ikubanni, P. P., Onu, P., Ogunniyi, O. J., & Adeleke, A. A. (2024). Smart home innovations—A mini review. En 2024 International Conference on Science, Engineering and Business for Driving Sustainable Development Goals (SEB4SDG) (pp. 1–6). IEEE. https://doi.org/10.1109/SEB4SDG60871.2024.10630398 eprints.lmu.edu.ng
23. Önder, M., Dogan, M. U., & Polat, K. (2023). Classification of smart grid stability prediction using cascade machine learning methods and the internet of things in smart grid. Neural Computing and Applications, 35(24), 17851–17869. https://doi.org/10.1007/s00521-023-08605-x DOI
24. Panzavecchia, N., Caravello, G., Cataliotti, A., Cosentino, V., Tinè, G., & Di Cara, D. (2025). Energy storage impact on CO2 emissions reduction in an islanded microgrid. IEEE Access, 13, 58887–58898. https://doi.org/10.1109/ACCESS.2025.3555883 iris.unipa.it
25. Rangarajan, S. S., Raman, R., Singh, A., Shiva, C. K., Kumar, R., Sadhu, P. K., Collins, E. R., & Senjyu, T. (2023). DC microgrids: A propitious smart grid paradigm for smart cities. Smart Cities, 6(4), 1690–1718. https://doi.org/10.3390/smartcities6040079 MDPI
26. Schreiber, J. F., Sausen, A., de Campos, M., Sausen, P. S., & da Silva Ferreira Filho, M. T. (2023). Data imputation techniques applied to the smart grids environment. IEEE Access, 11, 31931–31940. https://doi.org/10.1109/ACCESS.2023.3262188 ResearchGate
27. Singh, H., & Annapoorna, E. (2024). Fuzzy logic-based energy management in smart grids for renewable integration. MATEC Web of Conferences, 392, 01191. https://doi.org/10.1051/matecconf/202439201191 matec-conferences.org
28. Vandoorn, T. L., Renders, B., Degroote, L., Meersman, B., & Vandevelde, L. (2011). Active load control in islanded microgrids based on the grid voltage. IEEE Transactions on Smart Grid, 2(1), 139–151. https://doi.org/10.1109/TSG.2010.2090911 biblio.ugent.be
29. Yu, V. F., Le, T. H. A., & Gupta, J. N. D. (2022). Sustainable microgrid design with multiple demand areas and peer-to-peer energy trading involving seasonal factors and uncertainties. Renewable and Sustainable Energy Reviews, 161, 112342. https://doi.org/10.1016/j.rser.2022.112342 ScienceDirect
30. Wesley, B. J., Babu, G. S., & Kumar, P. S. (2024). Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources. Engineering Research Express, 6(1), 015074. https://doi.org/10.1088/2631-8695/ad1b71 colab.ws
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