DOI: https://doi.org/10.61435/ijred.2026.61709
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Multi objective building energy efficiency optimization scheduling by integrating multi objective grey wolf optimizer and long short term memory network

School of Architecture and Design, Lishui Vocational &Technical College, Lishui, 323000, China

Received: 18 Aug 2025; Revised: 16 Nov 2025; Accepted: 26 Dec 2025; Available online: 9 Jan 2026; Published: 1 Mar 2026.
Editor(s): H Hadiyanto
Open Access Copyright (c) 2026 The Author(s). Published by Centre of Biomass and Renewable Energy (CBIORE)
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

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Abstract

Building energy consumption accounts for a large proportion of the overall energy use in society, and energy-saving optimization scheduling is currently a research hotspot. However, traditional scheduling methods often struggle to achieve an effective balance between multiple objectives such as energy conservation, economy, and indoor comfort. To construct an energy-saving scheduling model that can consider multiple objectives, this paper puts forward a building energy scheduling model based on a Multi-Objective Grey Wolf Optimizer, taking total energy consumption, operating cost, and indoor comfort deviation as the optimization objectives. The model introduces a set of multi-energy collaborative constraints involving wind energy, photovoltaic systems, energy storage, and combined cooling, heating, and power systems. To improve algorithm performance, we innovatively integrated particle swarm optimization and simulated annealing to enhance the model's global search and local optimization capabilities, and introduced residual long short-term memory networks to improve load forecasting accuracy. Experimental results show that compared to similar models, the proposed algorithm improves the energy-saving rate by 13.3% in a typical household scenario. Its response time is 12 s, memory usage is 89 MB, and convergence speed is 42.86% faster than the slowest comparable model. The Multi-Objective Grey Wolf Optimizer effectively coordinates the multi-objective needs of building energy systems. It significantly improves energy savings and economic performance while ensuring indoor comfort. This algorithm provides strong support for intelligent building energy scheduling and offers practical value for promoting the carbon neutrality goals in the building sector.

Keywords: Multi-objective grey wolf optimizer; Particle swarm optimization; Residual network; Building energy efficiency; Simulated annealing

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Section: Original Research Article
Language : EN
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  1. Bai, Y., Zhou, D., Zhang, B., He, Z., & He, P. (2025). Two-to-one differential game via improved MOGWO. Journal of Systems Engineering and Electronics, 36(1), 233-255. https://doi.org/10.23919/JSEE.2025.000009
  2. Banerjee, A., & Banik, D. (2024). Resnet based hybrid convolution LSTM for hyperspectral image classification. Multimedia Tools and Applications, 83(15), 45059-45070. https://doi.org/10.1007/s11042-023-16241-9
  3. Chen, L., Fan, H., & Zhu, H. (2024). Multi-objective optimization of cancer treatment using the multi-objective gray wolf optimizer (MOGWO). Multiscale and Multidisciplinary Modeling, Experiments and Design, 7(3), 1857-1866. https://doi.org/10.1007/s41939-023-00307-0
  4. Choudhary, S., Sugumaran, S., Belazi, A., & El-Latif, A. A. A. (2023). Linearly decreasing inertia weight PSO and improved weight factor-based clustering algorithm for wireless sensor networks. Journal of ambient intelligence and humanized computing, 14(6), 6661-6679. https://doi.org/10.1007/s12652-021-03534-w
  5. Demir, S., & Sahin, E. K. (2023). Predicting occurrence of liquefaction-induced lateral spreading using gradient boosting algorithms integrated with particle swarm optimization: PSO-XGBoost, PSO-LightGBM, and PSO-CatBoost. Acta Geotechnica, 18(6), 3403-3419. https://doi.org/10.1007/s11440-022-01777-1
  6. Devianto, D., Ridho, S., Mutia, Y., Maiyastri, M., Asdi, Y., Permana, D., & Herdiani, E. T. (2025). The BVAR Model for Analyzing CO2 Emissions on Renewable Energy, Economic Growth, and Forest Area. Emerging Science Journal, 9(3), 1158-1173. https://doi.org/10.28991/ESJ-2025-09-03-02
  7. Du, Z., Ni, S., Pan, J. S., & Chu, S. (2025). A surrogate-assisted multi-objective grey wolf optimizer for empty-heavy train allocation considering coordinated line utilization balance. Journal of Bionic Engineering, 22(1), 383-397. https://doi.org/10.1007/s42235-024-00613-4
  8. Goyal, K. K., Sharma, N., Gupta, R. D., Gupta, S., Rani, D., Kumar, D., & Sharma, V. S. (2022). Measurement of performance characteristics of WEDM while processing AZ31 Mg-alloy using Levy flight MOGWO for orthopedic application. The International Journal of Advanced Manufacturing Technology, 119(11), 7175-7197. https://doi.org/10.1007/s00170-021-08358-8
  9. Heidari, A., Imani, D. M., Khalilzadeh, M., & Sarbazvatan, M. (2023). Green two-echelon closed and open location-routing problem: application of NSGA-II and MOGWO metaheuristic approaches. Environment, Development and Sustainability, 25(9), 9163-9199. https://doi.org/10.1007/s10668-022-02429-w
  10. Hosseini, S. E., Karimi, O., & AsemanBakhsh, M. A. (2024). Experimental investigation and multi-objective optimization of savonius wind turbine based on modified non-dominated sorting genetic algorithm-II. Wind Engineering, 48(3), 446-467. https://doi.org/10.1177/0309524X231217726
  11. Huang, J., & Chouvatut, V. (2024). Video-based sign language recognition via resnet and lstm network. Journal of Imaging, 10(6), 149-149. https://doi.org/10.3390/jimaging10060149
  12. Huang, Z., & Gou, Z. (2025). Occupancy and equipment usage prototype schedules for building energy simulations of office building types in China. Journal of Building Performance Simulation, 18(1), 56-75. https://doi.org/10.1080/19401493.2024.2422919
  13. Kamaruddin, M., Manullang, M. C., & Yee, J. J. (2025). Integrating Gradient Boosting and Parametric Architecture for Optimizing Energy Use Intensity in Net-Zero Energy Buildings. Civil Engineering Journal, 11(03), 910-931. https://doi.org/10.28991/CEJ-2025-011-03-06
  14. Kosanoglu, F., Atmis, M., & Turan, H. H. (2024). A deep reinforcement learning assisted simulated annealing algorithm for a maintenance planning problem. Annals of Operations Research, 339(1), 79-110. https://doi.org/10.1007/s10479-022-04612-8
  15. Kumar, A., Subbiah, R., Kukkala, V., Nagaraju, D. S., Upadhyay, C., & Karthikeyan, R. (2024). Experimental investigation and soft computing-based assessment using ann-mogwo-a hybrid approach for Inconel (825). Journal of Advanced Manufacturing Systems, 23(02), 305-327. https://doi.org/10.1142/S0219686724500136
  16. Lee, D., Kim, B., Kim, T., Joe, I., Chong, J., Min, K., & Jung, K. (2024). A ResNet-LSTM hybrid model for predicting epileptic seizures using a pretrained model with supervised contrastive learning. Scientific Reports, 14(1), 1319-1333. https://doi.org/10.1038/s41598-023-43328-y
  17. Li, D., Jiang, W., Qi, H., Wu, X., & Wu, Y. (2025). Influence of Grid Density and Rotation Model on Propeller Open Water CFD Simulation and Uncertainty. International Journal of Fluid Mechanics Research, 52(1). https://doi.org/10.1615/InterJFluidMechRes.2024054749
  18. Li, Z., Su, S., Jin, X., Xia, M., Chen, Q., & Yamashita, K. (2022). Stochastic and distributed optimal energy management of active distribution networks within integrated office buildings. CSEE Journal of Power and Energy Systems, 10(2), 504-517
  19. Liu, J., Liu, F., & Wang, L. (2024). Automated, economical, and environmentally-friendly asphalt mix design based on machine learning and multi-objective grey wolf optimization. Journal of Traffic and Transportation Engineering (English Edition), 11(3), 381-405. https://doi.org/10.1016/j.jtte.2023.10.002
  20. Mou, J., Gao, K., Duan, P., Li, J., Garg, A., & Sharma, R. (2022). A machine learning approach for energy-efficient intelligent transportation scheduling problem in a real-world dynamic circumstances. IEEE transactions on intelligent transportation systems, 24(12), 15527-15539 https://doi.org/10.1109/TITS.2022.3183215
  21. Nath, A. D., Abdelaty, A., Dilsiz, A. D., & Yamany, M. S. (2025). Energy Optimization in Residential Buildings: Evaluating PCM-CLT Wall Systems Across US Climate Zones. Civil Engineering Journal, 11(05), 1786-1806. https://doi.org/10.28991/CEJ-2025-011-05-05
  22. Navin Dhinnesh, A. D. C., & Sabapathi, T. (2024). Multi-objective Grey Wolf Optimization based self configuring wireless sensor network. Wireless Networks, 30(5), 3149-3160. https://doi.org/10.1007/s11276-024-03732-2
  23. Phong, P. D., Du, N. D., Hiep, P. H., & Thanh, T. X. (2022). A hybrid pso-sa scheme for improving accuracy of fuzzy time series forecasting models. Journal of Computer Science and Cybernetics, 38(3), 257-275. https://doi.org/10.15625/1813-9663/38/3/17424
  24. Poursaeid, M., Poursaeed, A. H., & Shabanlou, S. (2025). Water resources quality indicators monitoring by nonlinear programming and simulated annealing optimization with ensemble learning approaches. Water Resources Management, 39(3), 1073-1087. https://doi.org/10.1007/s11269-024-04006-4
  25. Rafay, A., Khan, U., Mujtaba, M., Javeed, A., Khalid, M. T., & Ali, Z. (2025). Optimizing Economic Load Dispatch Using a Hybrid PSO-SA Algorithm: A Novel Approach. International Journal of Innovations in Science & Technology, 7(7), 385-396
  26. Shekhar, H., Bhushan Mahato, C., Suman, S. K., Singh, S., Bhagyalakshmi, L., Prasad Sharma, M., Laxmi Kantha, B., Vidhya, T. H., Agraharam, S.K., & Rajaram, A. (2023). Demand side control for energy saving in renewable energy resources using deep learning optimization. Electric Power Components and Systems, 51(19), 2397-2413. https://doi.org/10.1080/15325008.2023.2246463
  27. Singh, Y. P., & Lobiyal, D. K. (2023). Automatic prediction of epileptic seizure using hybrid deep ResNet-LSTM model. AI communications, 36(1), 57-72. https://doi.org/10.3233/AIC-220177
  28. Sun, Y. (2025). A demand side energy scheduling method for energy-saving buildings based on priority weights. International Journal of Global Energy Issues, 47(4-5), 466-479. https://doi.org/10.1504/IJGEI.2025.147241
  29. Sylejmani, K., Gashi, E., & Ymeri, A. (2023). Simulated annealing with penalization for university course timetabling. Journal of Scheduling, 26(5), 497-517. https://doi.org/10.1007/s10951-022-00747-5
  30. Tang, H., & Wang, S. (2022). Multi-level optimal dispatch strategy and profit-sharing mechanism for unlocking energy flexibilities of non-residential building clusters in electricity markets of multiple flexibility services. Renewable Energy, 201, 35-45. https://doi.org/10.1016/j.renene.2022.10.089
  31. Torabi, A., Yosefvand, F., Shabanlou, S., Rajabi, A., & Yaghoubi, B. (2024). Optimization of integrated operation of surface and groundwater resources using multi-objective grey wolf optimizer (MOGWO) algorithm. Water Resources Management, 38(6), 2079-2099. https://doi.org/10.1007/s11269-024-03744-9
  32. Usman, A. M., & Abdullah, M. K. (2023). An assessment of building energy consumption characteristics using analytical energy and carbon footprint assessment model. Green and Low-Carbon Economy, 1(1), 28-40. https://doi.org/10.47852/bonviewGLCE3202545
  33. Wei, L., Diao, Q., Sun, Y., Li, M., & Liu, H. (2025). State of health estimation based on PSO-SA-LSTM for fast-charge lithium-ion batteries. Ionics, 31(1), 367-383. https://doi.org/10.1007/s11581-024-05954-y
  34. Yu, S., Zhang, Z., Wang, S., Huang, X., & Lei, Q. (2024). A performance-based hybrid deep learning model for predicting TBM advance rate using attention-ResNet-LSTM. Journal of Rock Mechanics and Geotechnical Engineering, 16(1), 65-80. https://doi.org/10.1016/j.jrmge.2023.06.010
  35. Zhang, B., Xia, Y., & Peng, X. (2024). Robust optimal dispatch strategy of integrated energy system considering CHP-P2G-CCS. Global Energy Interconnection, 7(1), 14-24. https://doi.org/10.1016/j.gloei.2024.01.002
  36. Zhao, F., Di, S., & Wang, L. (2022). A hyperheuristic with Q-learning for the multiobjective energy-efficient distributed blocking flow shop scheduling problem. IEEE Transactions on Cybernetics, 53(5), 3337-3350. https://doi.org/10.1109/TCYB.2022.3192112
  37. Zhao, F., Hu, X., Wang, L., Xu, T., Zhu, N., & Jonrinaldi. (2023). A reinforcement learning-driven brain storm optimisation algorithm for multi-objective energy-efficient distributed assembly no-wait flow shop scheduling problem. International Journal of Production Research, 61(9), 2854-2872. https://doi.org/10.1080/00207543.2022.2070786
  38. Zheng, W., Li, J., Lei, K., Shao, Z., Li, J., & Xu, Z. (2023). Optimal dispatch of HCNG penetrated integrated energy system based on modelling of HCNG process. International Journal of Hydrogen Energy, 48(51), 19437-19449. https://doi.org/10.1016/j.ijhydene.2023.02.056
  39. Zhou, M., Liu, S., & Li, J. (2023). Multi-scale forest flame detection based on improved and optimized YOLOv5. Fire Technology, 59(6), 3689-3708. https://doi.org/10.1007/s10694-023-01486-5
  40. Zhu, S., Ota, K., & Dong, M. (2022). Energy-efficient artificial intelligence of things with intelligent edge. IEEE Internet of Things Journal, 9(10), 7525-7532. https://doi.org/10.1109/JIOT.2022.3143722

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