Improving Energy and Comfort Management in Smart Buildings | ||
| پژوهشنامه اقتصاد انرژی ایران | ||
| Volume 12, Issue 45 - Serial Number 1, January 2023, Pages 71-93 PDF (1.06 M) | ||
| Document Type: Research Paper | ||
| DOI: 10.22054/jiee.2023.72590.1986 | ||
| Authors | ||
| Akram Beigi* 1; Fariba Fatahi2 | ||
| 1Assistant Professor, Department of Computer Engineering, Shahid Rajaee Teacher Training University, Tehran, Iran | ||
| 2M.Sc. Student in Artificial Intelligence, Shahid Rajaee Teacher Training University, Tehran, Iran | ||
| Abstract | ||
| Managing energy consumption in smart buildings has become an increasingly important challenge. Efficient energy management can have a positive impact on both micro and macroeconomics. Moreover, it is essential to ensure that the comfort of smart building residents is maintained at an acceptable level. Optimization algorithms can be used to achieve user convenience while minimizing energy consumption. In this study, we propose an optimization approach that utilizes an agent-based architecture. This architecture comprises intelligent agents that communicate with each other via message exchange in a network structure consisting of three layers: (1) The switch layer monitors user preferences and comfort levels. (2) The coordination layer includes a coordinating agent that determines the optimal timing for electrical appliances to minimize electricity consumption costs and maximize user comfort. (3) The execution layer contains performer agents. Our focus in this research is on the coordination layer with the aim of reducing energy consumption costs and peak average rates, while increasing user comfort to the highest possible level. However, this optimization problem is highly complex due to the large number of electrical devices and their capabilities. To address this, we propose a hybrid method based on genetic and bat algorithms. We evaluated its performance based on objective functions and compared it with recent research on SmartHome and CU-Bems datasets. Our results demonstrate an improvement in performance | ||
| Keywords | ||
| Energy Consumption Management; User Comfort; Multi-Objective Optimization; Genetic Algorithm; Bat Algorithm | ||
| References | ||
|
اللهیاری، افق، بایرامی راد، وحید و اسلام نژادنمین، مجتبی. (1399). بررسی اهمیت هوش مصنوعی در ساختمانهای هوشمند. هشتمین کنفرانس ملی مهندسی برق، کامپیوتر و مکانیک، شیروان.
بهروزی، امیر، عبدیزدان، مرجان و نوری مهر، محمدرضا. (1398). ارائه الگوریتم فازی ـ عصبی مبتنی بر اینترنت اشیا جهت بهینه سازی انرژی در ساختمان هوشمند. پنجمین کنفرانس ملی محاسبات توزیعی و پردازش دادههای بزرگ، تبریز.
سعیدخانی، محمد، فلاحی، اسماعیل وبانشی، مهدی. (1395). ارائة مدل مدیریت تأمین انرژی در ایران براساس معیارهای فنی، اقتصادی و زیستمحیطی. پژوهشنامه اقتصاد انرژی ایران، 5(18)، 29-60. doi: 10.22054/jiee.2016.7192.
شریفیان، سمانه سادات، پهلوانزاده، نادر و کوه بنانی، حسین. (1399). کنترل هوشمند مصرف انرژی ساختمان در یک تالار پذیرایی با استفاده از منطق فازی. پنجمین همایش بینالمللی افقهای نوین در مهندسی برق، کامپیوتر و مکانیک، تهران.
صانعی، ساره و حاج محمدی، آیدا. (1399). بررسی اینترنت اشیا و کاربرد و اهمیت آن در ساختمانها و شهرهای هوشمند. پنجمین کنفرانس بینالمللی مهندسی برق،کامپیوتر و مکانیک، تهران.
عباسی، علی. (1399). پیادهسازی عملی اینترنت اشیاء در یک ساختمان هوشمند. پنجمین همایش بینالمللی افقهای نوین در مهندسی برق، کامپیوتر و مکانیک، تهران.
Abbasi, A. (2020). Practical implementation of Internet of Things in a smart building. In 5th Conference on the New Horizons in the Electrical Engineering, Computer and Mechanic. Tehran. https://civilica.com/doc/ 1042856. [In Persian]
Ahmad, A., Javaid, N., Alrajeh, N., Khan, Z.A., Qasim, U. & Khan, A. (2015). A Modified Feature Selection and Artificial Neural Network-Based Day-Ahead Load Forecasting Model for a Smart Grid. Applied Sciences, 5(4),1756-1772. https://doi.org/10.3390/app5041756.
Allah-yari, O., Bayrami-rad, V. & Eslamnejad-namin, M. (2020). Investigating the importance of artificial intelligence in smart buildings. In 8th Conference on Electrical, Computer and Mechanical Engineering, Shirvan. https://civilica.com/doc/1043280. [In Persian]
Alsalibi, B., Abualigah, L. & Khader, A. T. (2021). A novel bat algorithm with dynamic membrane structure for optimization problems. Applied Intelligence, 51, 1992-2017. https://doi.org/10.1007/s10489-020-01898-8.
Anvari-Moghaddam, A., Rahimi-Kian, A., Mirian, M. S. & Guerrero, J. M. (2017). A multi-agent based energy management solution for integrated buildings and microgrid system. Applied energy, 203, 41-56. https://doi.org/10.1016/j.apenergy.2017.06.007.
Badar, A. Q. & Anvari-Moghaddam, A. (2022). Smart home energy management system–a review, Advances in Building Energy Research, 16(1),118-143. https://doi.org/10.1080/17512549.2020.1806925.
Behrouzi, A., Abd-yazdan, M. & Noorimehr, M. R. (2019). A fuzzy-neural algorithm based on Internet of Things for energy optimization in smart building. In 5th Conference on Distributed Computing and Big Data Processing, Tabriz. https://civilica.com/doc/961944. [In Persian]
Brahim, G. B. (2021). Weather Conditions Impact on Electricity Consumption in Smart Homes: Machine Learning Based Prediction Model. In 8th International Conference on Electrical and Electronics Engineering (ICEEE), pp. 93-98, IEEE.
Chumnanvanichkul, P., Chirapongsananurak, P. & Hoonchareon, N. (2019). Three-level Classification of Air Conditioning Energy Consumption for Building Energy Management System Using Data Mining Techniques. In 2019 IEEE PES GTD Grand International Conference and Exposition Asia, pp. 611-615, IEEE.
Dashtaki, A. A., Khaki, M., Zand, M., Nasab, M. A., Sanjeevikumar, P., Samavat, T. & Khan, B. (2022). A Day Ahead Electrical Appliance Planning of Residential Units in a Smart Home Network Using ITS-BF Algorithm. International Transactions on Electrical Energy Systems, Vol. 2022, Article ID 2549887, pp. 1-13. https://doi.org/10.1155/2022/ 2549887.
Degha, H. E., Laallam, F. Z. & Said, B. (2019). Intelligent context-awareness system for energy efficiency in smart building based on ontology. Sustainable computing: informatics and systems, 21, 212-233. https://doi.org/10.1016/j.suscom.2019.01.013.
González-Briones, A., De La Prieta, F., Mohamad, M. S., Omatu, S. & Corchado, J. M. (2018). Multi-agent systems applications in energy optimization problems: A state-of-the-art review. Energies, 11(8), 1928. https://doi.org/10.3390/en11081928.
Hurtado, L. A., Nguyen, P. H. & Kling, W. L. (2015). Smart grid and smart building inter-operation using agent-based particle swarm optimization. Sustainable Energy, Grids and Networks, 2, 32-40. https://doi.org/ 10.1016/j.segan.2015.03.003.
Khalid, R., Javaid, N., Rahim, M. H., Aslam, S. & Sher, A. (2019). Fuzzy energy management controller and scheduler for smart homes. Sustainable Computing: Informatics and Systems, 21, 103-118. https://doi.org/10.1016/j.suscom.2018.11.010.
Khan, F. A., Ullah, K. & Anwar, S. (2022). Energy optimization in smart urban buildings using bio-inspired ant colony optimization. Soft Computing, 27, 973–989. https://doi.org/10.1007/s00500-022-07537-3.
Malik, S. & Kim, D. (2018). Prediction-learning algorithm for efficient energy consumption in smart buildings based on particle regeneration and velocity boost in particle swarm optimization neural networks. Energies, 11(5), 1289. https://doi.org/10.3390/en11051289.
Miller, C. & Tian, J. (2020). CU-BEMS, smart building energy and IAQ data. Retrieved January, 2020, from https://www.kaggle.com/datasets/ claytonmiller/cubems-smart-building-energy-and-iaq-data.
Mohanty, S. & Dash, R. (2022). A comprehensive review on bio-inspired flower pollination algorithm. Journal of Information and Optimization Sciences, 43(5), 963-971. https://doi.org/10.1080/02522667.2022.2092224.
Netto, R. S., Ramalho, G. R., Bonatto, B. D., Carpinteiro, O. A., Zambroni de Souza, A. C., Oliveira, D. Q. & Braga, R. A. (2018). Real-time framework for energy management system of a smart microgrid using multiagent systems. energies, 11(3), 656. https://doi.org/10.3390/en11030656.
Rahim, S., Iqbal, Z., Shaheen, N., Khan, Z. A., Qasim, U., Khan, S. A. & Javaid, N. (2016). Ant colony optimization based energy management controller for smart grid. In 30th International Conference on Advanced Information Networking and Applications (AINA), pp. 1154-1159, IEEE.
Roth, A. & Reyna, J. (2019). Grid-interactive efficient buildings technical report series: Whole-building controls, sensors, modeling, and analytics, (No. NREL/TP-5500-75478; DOE/GO-102019-5230). USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office, Washington DC (United States).
Saeed Khani, M., Fallahi, E. & Baneshi, M. (2016). Modeling for Energy Supply Management in Iran Based on Technical, Economic and Environmental Criteria. Iranian Energy Economics, 5(18), pp. 29-60. https://10.22054/jiee.2016.7192. [In Persian]
Sanei, S. & Hajmohamadi, A. (2020). Investigating the importance of artificial intelligence in smart buildings. In 5th Conference on Electrical, Computer and Mechanical Engineering, Tehran. https://civilica.com/doc/1039792. [In Persian]
Shah, A. S., Nasir, H., Fayaz, M., Lajis, A. & Shah, A. (2019). A review on energy consumption optimization techniques in IoT based smart building environments. Information, 10(3), 108. https://doi.org/10.3390/ info10030108.
Sharifian, S. S., Pahlevanzadeh, N. & Koohbanani, H. (2020). Intelligent control of building energy consumption in a reception hall using fuzzy logic. In 5th Conference on the New Horizons in the Electrical Engineering, Computer and Mechanic. Tehran. https://civilica.com/doc/1042821. [In Persian]
Singh A. T. (2020). Smart Home Dataset with weather Information. Retrieved January, 2020, from https://www.kaggle.com/datasets/taranvee/ smart-home-dataset-with-weather-information.
Wendler, T. & Gröttrup, S., (2016). Data mining with SPSS modeler: theory, exercises and solutions, Springer.
Zaouali, K., Ammari, M. L., Tabka, M., Choueib, A. & Bouallegue, R. (2018). Smart Home Resource Management Based on Multi-Agent System Modeling Combined with SVM Machine Learning for Prediction and Decision-Making. The Eleventh International Conference on Advances in Computer-Human Interactions, pp. 120-27. | ||
|
Statistics Article View: 1,002 PDF Download: 653 |
||