Machine Learning en la Optimización del Consumo de Energía en Edificios Inteligentes: Tendencias y Desafíos

Jorge Luis  Veloz Zambrano; Yván Jesús Túpac Valdivia; Augusto Bernuy Alva

doi:10.56124/encriptar.v8i15.011

Authors

Jorge Luis Veloz Zambrano Universidad Nacional Mayor San Marcos
Yván Jesús Túpac Valdivia Universidad Católica San Pablo
Augusto Bernuy Alva Universidad de San Martin de Porres

DOI:

https://doi.org/10.56124/encriptar.v8i15.011

Keywords:

electrical energy, consumption, forecast, optimization

Abstract

At the forefront of the technological and sustainable revolution, smart buildings stand out as models of efficiency and comfort, with a projected growth of 45 million in 2022 to 115 million by 2026. These buildings use advanced technology to optimize resources, improving the safety and well-being of their occupants. However, they face challenges in urban infrastructure, balancing accuracy, scalability, and adaptability in the field of the Internet of Things. The importance of data and machine learning to predict and improve energy consumption is emphasized, addressing the need for efficient and practical predictive models that handle complex data and capture spatiotemporal patterns. Gaps are identified in the standardized comparison of models, the efficiency of predictive algorithms, and the transformation of research into practical applications. This study asks key questions about resource management and optimizing energy consumption through machine learning, proposing specific objectives such as data collection and evaluation of models to validate the effectiveness and improve the lives of occupants, as well as contribute to the economic, environmental, and social impact. A systematic methodology for the literature review is followed, employing the PRISMA statement, and research questions are posed to guide the identification of trends and responses to the use of machine learning to predict energy consumption in smart buildings.

Downloads

Download data is not yet available.

References

Abaimov, S., & Martellini, M. (2022). Understanding Machine Learning. In Advanced Sciences and Technologies for Security Applications (pp. 15–89). Springer. https://doi.org/10.1007/978-3-030-91585-8_2

Ahmad, I., Shahabuddin, S., Sauter, T., Harjula, E., Kumar, T., Meisel, M., Juntti, M., & Ylianttila, M. (2021). The Challenges of Artificial Intelligence in Wireless Networks for the Internet of Things: Exploring Opportunities for Growth. IEEE Industrial Electronics Magazine, 15(1), 16–29. https://doi.org/10.1109/MIE.2020.2979272

Ahmed, M. A., Chavez, S. A., Eltamaly, A. M., Garces, H. O., Rojas, A. J., & Kim, Y.-C. (2022). Toward an Intelligent Campus: IoT Platform for Remote Monitoring and Control of Smart Buildings. Sensors, 22(23). https://doi.org/10.3390/s22239045

Al-Shargabi, A. A., Almhafdy, A., Ibrahim, D. M., Alghieth, M., & Chiclana, F. (2022). Buildings’ energy consumption prediction models based on buildings’ characteristics: Research trends, taxonomy, and performance measures. Journal of Building Engineering, 54. https://doi.org/10.1016/j.jobe.2022.104577

Annadurai, C., Nelson, I., Devi, K. N., Manikandan, R., Jhanjhi, N. Z., Masud, M., & Sheikh, A. (2022). Biometric Authentication-Based Intrusion Detection Using Artificial Intelligence Internet of Things in Smart City. Energies, 15(19). https://doi.org/10.3390/en15197430

Balaji, S., & Karthik, S. (2023). Energy Prediction in IoT Systems Using Machine Learning Models. Computers, Materials and Continua, 75(1), 443–459. https://doi.org/10.32604/cmc.2023.035275

Barker, O. (2020). Realizing the Promise of the Internet of Things in Smart Buildings. Computer, 53(2), 76–79. https://doi.org/10.1109/MC.2019.2952419

Bedi, G., Venayagamoorthy, G. K., & Singh, R. (2020). Development of an IoT-driven building environment for prediction of electric energy consumption. IEEE Internet of Things Journal, 7(6), 4912–4921. https://doi.org/10.1109/JIOT.2020.2975847

Blechmann, S., Sowa, I., Schraven, M. H., Streblow, R., Müller, D., & Monti, A. (2023). Open source platform application for smart building and smart grid controls. Automation in Construction, 145. https://doi.org/10.1016/j.autcon.2022.104622

Bourdeau, M., qiang Zhai, X., Nefzaoui, E., Guo, X., & Chatellier, P. (2019). Modeling and forecasting building energy consumption: A review of data-driven techniques. In Sustainable Cities and Society (Vol. 48). Elsevier Ltd. https://doi.org/10.1016/j.scs.2019.101533

Broday, E. E., & da Silva, M. C. G. (2023). The role of internet of things (IoT) in the assessment and communication of indoor environmental quality (IEQ) in buildings: a review. Smart and Sustainable Built Environment, 12(3), 584–606. https://doi.org/10.1108/SASBE-10-2021-0185

Carli, R., Cavone, G., Othman, S. B., & Dotoli, M. (2020). IoT based architecture for model predictive control of HVAC systems in smart buildings. Sensors (Switzerland), 20(3). https://doi.org/10.3390/s20030781

Casado-Vara, R., Rey, A. M., Affes, S., Prieto, J., & Corchado, J. M. (2020). IoT network slicing on virtual layers of homogeneous data for improved algorithm operation in smart buildings. Future Generation Computer Systems, 102, 965–977. https://doi.org/10.1016/j.future.2019.09.042

Choi, H.-S., & Rhee, W.-S. (2014). Iot-based user-driven service modeling environment for a smart space management system. Sensors (Switzerland), 14(11), 22039–22064. https://doi.org/10.3390/s141122039

Chou, J.-S., & Tran, D.-S. (2018). Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders. Energy, 165, 709–726. https://doi.org/10.1016/j.energy.2018.09.144

Do, H., & Cetin, K. S. (2018). Residential Building Energy Consumption: a Review of Energy Data Availability, Characteristics, and Energy Performance Prediction Methods. Current Sustainable/Renewable Energy Reports, 5(1), 76 – 85. https://doi.org/10.1007/s40518-018-0099-3

Dylan, T., Durrant, A. C., & Cerci, S. (2021). Lanterns configuring a digital resource to inspire preschool children’s free play outdoors. Conference on Human Factors in Computing Systems - Proceedings. https://doi.org/10.1145/3411764.3445745

González-Vidal, A., Jiménez, F., & Gómez-Skarmeta, A. F. (2019). A methodology for energy multivariate time series forecasting in smart buildings based on feature selection. Energy and Buildings, 196, 71–82. https://doi.org/10.1016/j.enbuild.2019.05.021

Hernández-Callejo, L., Gómez, A., Nesmachnow, S., Leite, V., Prieto, J., & Ferreira, Â. (n.d.). CITIES Energetic Efficiency, Sustainability; Infrastructures, Energy and the Environment; Mobility and IoT; Governance and Citizenship. www.mdpi.com/journal/applsci

Hoy, M. B. (2016). Smart Buildings: An Introduction to the Library of the Future. Medical Reference Services Quarterly, 35(3), 326–331. https://doi.org/10.1080/02763869.2016.1189787

Jiang, J., Liu, F., Ng, W. W. Y., Tang, Q., Wang, W., & Pham, Q.-V. (2022). Dynamic Incremental Ensemble Fuzzy Classifier for Data Streams in Green Internet of Things. IEEE Transactions on Green Communications and Networking, 6(3), 1316–1329. https://doi.org/10.1109/TGCN.2022.3151716

Juniper Research. (2024, November 10). Smart Buildings Market Trends, Size, Strategies 2024-29.

Khanna, A., Arora, S., Chhabra, A., Bhardwaj, K. K., & Sharma, D. K. (2019). IoT architecture for preventive energy conservation of smart buildings. In Studies in Systems, Decision and Control (Vol. 206, pp. 179–208). Springer International Publishing. https://doi.org/10.1007/978-981-13-7399-2_8

Khaoula, E., Amine, B., & Mostafa, B. (2023). Evaluation and Comparison of Energy Consumption Prediction Models Case Study: Smart Home. In Lecture Notes on Data Engineering and Communications Technologies (Vol. 164, pp. 179–187). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1007/978-3-031-27762-7_17

Kim, J., Jeon, Y., & Kim, H. (2018). The intelligent IoT common service platform architecture and service implementation. Journal of Supercomputing, 74(9), 4242–4260. https://doi.org/10.1007/s11227-016-1845-1

Kumar, A., Sharma, S., Goyal, N., Singh, A., Cheng, X., & Singh, P. (2021). Secure and energy-efficient smart building architecture with emerging technology IoT. Computer Communications, 176, 207–217. https://doi.org/10.1016/j.comcom.2021.06.003

Kuo, S.-Y., Huang, X.-R., & Chen, L.-B. (2022). Smart ports: Sustainable smart business port operation schemes based on the Artificial Intelligence of Things and blockchain technologies. IEEE Potentials, 41(6), 32–37. https://doi.org/10.1109/MPOT.2022.3198808

Lee, C.-T., Chen, L.-B., Chu, H.-M., & Hsieh, C.-J. (2022). Design and Implementation of a Leader-Follower Smart Office Lighting Control System Based on IoT Technology. IEEE Access, 10, 28066–28079. https://doi.org/10.1109/ACCESS.2022.3158494

Li, K., Zhao, J., Hu, J., & Chen, Y. (2022). Dynamic energy efficient task offloading and resource allocation for NOMA-enabled IoT in smart buildings and environment. Building and Environment, 226. https://doi.org/10.1016/j.buildenv.2022.109513

Li, W., Li, H., & Wang, S. (2021). An event-driven multi-agent based distributed optimal control strategy for HVAC systems in IoT-enabled smart buildings. Automation in Construction, 132. https://doi.org/10.1016/j.autcon.2021.103919

Louridas, P., & Ebert, C. (2016). Machine Learning. IEEE Software, 33(5), 110–115. https://doi.org/10.1109/MS.2016.114

Maatoug, A., Belalem, G., & Mahmoudi, S. (2023). A location-based fog computing optimization of energy management in smart buildings: DEVS modeling and design of connected objects. Frontiers of Computer Science, 17(2). https://doi.org/10.1007/s11704-021-0375-z

Medhat, M., El-Shafey, K., & Rashed, A. (2020). IoT-fog based smart-building security system design and performance evaluation. Journal of Computer Science, 16(9), 1325–1333. https://doi.org/10.3844/jcssp.2020.1325.1333

Metallidou, C. K., Psannis, K. E., & Egyptiadou, E. A. (2020). Energy Efficiency in Smart Buildings: IoT Approaches. IEEE Access, 8, 63679–63699. https://doi.org/10.1109/ACCESS.2020.2984461

Moura, P., Moreno, J. I., López, G. L., & Alvarez-Campana, M. (2021). IoT platform for energy sustainability in university campuses. Sensors (Switzerland), 21(2), 1–22. https://doi.org/10.3390/s21020357

Pašek, J., & Sojková, V. (2018). Facility management of smart buildings. International Review of Applied Sciences and Engineering, 9(2), 181–187. https://doi.org/10.1556/1848.2018.9.2.15

Rajamohan, K., Rangasamy, S., Pinto, N. A., Manoj, B. E., Mukherjee, D., & Shukla, J. (2023). IoVST: Internet of vehicles and smart traffic - Architecture, applications, and challenges. In Handbook of Research on Machine Learning-Enabled IoT for Smart Applications Across Industries (pp. 292–315). IGI Global. https://doi.org/10.4018/978-1-6684-8785-3.ch015

Rajaoarisoa, L., M’Sirdi, N. K., Sayed-Mouchaweh, M., & Clavier, L. (2023). Decentralized fault-tolerant controller based on cooperative smart-wireless sensors in large-scale buildings. Journal of Network and Computer Applications, 214. https://doi.org/10.1016/j.jnca.2023.103605

Rico, A., Smuts, C., & Larson, K. (2022). Chameleon: Adaptive Sensor Intelligence for Smart Buildings. IEEE Internet of Things Journal, 9(19), 19362–19372. https://doi.org/10.1109/JIOT.2022.3165349

Shi, C., Liu, P., Chen, Y., Zhou, Z., Yang, J., Zhao, C., Chen, B., Yang, S., & Mumtaz, S. (2022). Adversarial learning-based multi-timescale network resource management in multi-mode green IoT network for smart building. IET Communications, 16(14), 1739–1751. https://doi.org/10.1049/cmu2.12441

Silva, C., Costa, N., Grilo, C., & Veloz, J. (2018). JavaScript middleware for mobile agents support on desktop and mobile platforms. In Advances in Intelligent Systems and Computing (Vol. 721). https://doi.org/10.1007/978-3-319-73450-7_70

Somu, N., R, G. R. M., & Ramamritham, K. (2021). A deep learning framework for building energy consumption forecast. Renewable and Sustainable Energy Reviews, 137. https://doi.org/10.1016/j.rser.2020.110591

Sun, J., Kuruganti, T., Fricke, B., Xuan, S., Li, Y., Wilkerson, W., & Cunningham, C. (2022). Automated fault detection and diagnosis deployment Internet of Things solution for building energy system. Journal of Building Engineering, 61. https://doi.org/10.1016/j.jobe.2022.105291

Sun, Y., Wu, T.-Y., Li, X., & Guizani, M. (2017). A Rule Verification System for Smart Buildings. IEEE Transactions on Emerging Topics in Computing, 5(3), 367–379. https://doi.org/10.1109/TETC.2016.2531288

Wang, M., Yeh, W.-C., Chu, T.-C., Zhang, X., Huang, C.-L., & Yang, J. (2018). Solving multi-objective fuzzy optimization in wireless smart sensor networks under uncertainty using a hybrid of IFR and SSO algorithm. Energies, 11(9). https://doi.org/10.3390/en11092385

Wang, W.-C., Dwijendra, N. K. A., Sayed, B. T., Alvarez, J. R. N., Al-Bahrani, M., Alviz-Meza, A., & Cárdenas-Escrocia, Y. (2023). Internet of Things Energy Consumption Optimization in Buildings: A Step toward Sustainability. Sustainability (Switzerland), 15(8). https://doi.org/10.3390/su15086475

Wang, Z., Liu, J., Zhang, Y., Yuan, H., Zhang, R., & Srinivasan, R. S. (2021). Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles. In Renewable and Sustainable Energy Reviews (Vol. 143). Elsevier Ltd. https://doi.org/10.1016/j.rser.2021.110929

Wang, Z., & Srinivasan, R. S. (2017). A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. In Renewable and Sustainable Energy Reviews (Vol. 75, pp. 796–808). Elsevier Ltd. https://doi.org/10.1016/j.rser.2016.10.079

Yan, Y. (2022). Machine Learning Fundamentals. In Machine Learning in Chemical Safety and Health: Fundamentals with Applications (pp. 19–46). wiley. https://doi.org/10.1002/9781119817512.ch2

Zekić-Sušac, M., Has, A., & Knežević, M. (2021). Predicting energy cost of public buildings by artificial neural networks, CART, and random forest. Neurocomputing, 439, 223–233. https://doi.org/10.1016/j.neucom.2020.01.124