A Multitask Framework for Optimizing Smart Grid Energy Consumption Using RegClassXNet and Dynamic Cluster Adjustment
Abstract
With urbanization accelerating electricity demand, advanced energy management is vital for building sustainable cities. This paper presents a novel framework using real-world data from commercial, residential, and industrial buildings over six years to optimize electricity consumption. The proposed RegClassXNet model, integrating EfficientNet, Xception, and Swin-Transformer, performs multitask predictions for both classification and regression objectives. Proportional Dynamic Cluster Adjustment (PDCA) is introduced to address data imbalance, and a hybrid attribute refinement process synthesizes relevant features to enhance predictive accuracy. Our model achieves 95.0% R-squared, 2.1 MAE, 1.8 RMSE, and 3.2 MSE, significantly outperforming existing methods such as CNN, LSTM, and RF. New stability metrics, including Label Variability Consistency Index (LVCI), Temporal Prediction Stability Measure (TPSM), and Output Correlation Coefficient (OCC), ensure robust and consistent predictions. The framework was evaluated on a dataset comprising 500,000 energy consumption records, utilizing a distributed training approach on a high-performance GPU cluster. Simulations illustrate the framework’s capability to optimize energy usage across building types, adjust for environmental impacts, and support effective energy-saving strategies. This work offers a transformative approach to sustainable energy management, paving the way for adaptive, data-driven smart grid systems.
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C. Wang, J. Song, D. Shi, J. L. Reyna, H. Horsey, S. Feron, and R. B. Jackson (2023) Impacts of climate change, population growth, and power sector decarbonization on urban building energy use, Nature Communications, Publisher, vol. 14, no. 1, pp. 1-16.
S. Mischos, E. Dalagdi, and D. Vrakas (2023) Intelligent energy management systems: a review, Artificial Intelligence Review, Publisher, vol. 56, no. 10, pp. 11635-11674.
L. A. Yousef, H. Yousef, and L. Rocha-Meneses (2023) Artificial intelligence for management of variable renewable energy systems: a review of current status and future directions, Energies, Publisher, vol. 16, no. 24, p. 8057.
I. B. Dhaou (2023) Design and Implementation of an Internet-of-Things-Enabled Smart Meter and Smart Plug for Home-Energy-Management System, Electronics, Publisher, vol. 12, no. 19, p. 4041.
P. Krishnan, A. V. Prabu, S. Loganathan, S. Routray, U. Ghosh, and M. AL-Numay (2023) Analyzing and managing various energy-related environmental factors for providing personalized IoT services for smart buildings in smart environment, Sustainability, Publisher, vol. 15, no. 8, p. 6548.
F. Qayyum, H. Jamil, and F. Ali (2023) A Review of Smart Energy Management in Residential Buildings for Smart Cities, Energies, Publisher, vol. 17, no. 1, p. 83.
S. U. Khan, N. Khan, F. U. M. Ullah, M. J. Kim, M. Y. Lee, and S. W. Baik (2023) Towards intelligent building energy management: AI-based framework for power consumption and generation forecasting, Energy and Buildings, Publisher, vol. 279, p. 112705.
M. U. Saleem, M. Shakir, M. R. Usman, M. H. T. Bajwa, N. Shabbir, P. Shams Ghahfarokhi, and K. Daniel (2023) Integrating smart energy management system with internet of things and cloud computing for efficient demand side management in smart grids, Energies, Publisher, vol. 16, no. 12, p. 4835.
M. Irfan, N. Ayub, F. Althobiani, S. Masood, Q. A. Arbab Ahmed, M. H. Saeed, and S. N. Faraj Mursal (2023) Ensemble learning approach for advanced metering infrastructure in future smart grids, PLOS ONE, Publisher, vol. 18, no. 10, p. e0289672.
J. F. Torres, F. Martínez-Álvarez, and A. Troncoso (2022) A deep LSTM network for the Spanish electricity consumption forecasting, Neural Computing and Applications, Publisher, vol. 34, no. 13, pp. 10533-10545.
A. A. Almazroi and N. Ayub (2023) Multi-task learning for electricity price forecasting and resource management in cloud-based industrial IoT systems, IEEE Access, Publisher, vol. 11, pp. 54280-54295.
Z. Fazlipour, E. Mashhour, and M. Joorabian (2022) A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism, Applied Energy, Publisher, vol. 327, p. 120063.
A. Kathirgamanathan, A. Patel, A. S. Khwaja, B. Venkatesh, and A. Anpalagan (2022) Performance comparison of single and ensemble CNN, LSTM, and traditional ANN models for short-term electricity load forecasting, The Journal of Engineering, Publisher, no. 5, pp. 550-565.
L. Dai (2024) Performance analysis of deep learning-based electric load forecasting model with particle swarm optimization, Heliyon, Publisher, vol. 10, no. 16.
M. F. Alsharekh, S. Habib, D. A. Dewi, W. Albattah, M. Islam, and S. Albahli (2022) Improving the efficiency of multistep short-term electricity load forecasting via R-CNN with ML-LSTM, Sensors, Publisher, vol. 22, no. 18, p. 6913.
V. Ramamoorthi (2022) Optimizing Cloud Load Forecasting with a CNN-BiLSTM Hybrid Model, International Journal of Intelligent Automation and Computing, Publisher, vol. 5, no. 2, pp. 79-91.
X. Wang, H. Wang, B. Bhandari, and L. Cheng (2024) AI-empowered methods for smart energy consumption: A review of load forecasting, anomaly detection, and demand response, International Journal of Precision Engineering and Manufacturing-Green Technology, Publisher, vol. 11, no. 3, pp. 963-993.
N. Ahmad, Y. Ghadi, M. Adnan, and M. Ali (2022) Load forecasting techniques for power system: Research challenges and survey, IEEE Access, Publisher, vol. 10, pp. 71054-71090.
M. Alizamir, J. Shiri, A. F. Fard, S. Kim, A. D. Gorgij, S. Heddam, and V. P. Singh (2023) Improving the accuracy of daily solar radiation prediction by climatic data using an efficient hybrid deep learning model: Long short-term memory (LSTM) network coupled with wavelet transform, Engineering Applications of Artificial Intelligence, Publisher, vol. 123, p. 106199.
S. E. Ahmadi and S. Delpasand (2023) IoT‐Based Energy Management Strategies in Smart Grid, Smart Grids and Internet of Things: An Energy Perspective, Publisher, pp. 91-126.
A. A. Almazroi, F. S. Alsubaei, N. Ayub, and N. Z. Jhanjhi (2024) Inclusive Smart Cities: IoT-Cloud Solutions for Enhanced Energy Analytics and Safety, International Journal of Advanced Computer Science & Applications, Publisher, vol. 15, no. 5.
Caleb (2024) Southern California Energy Consumption [Data set], Kaggle, Publisher. [Online]. Available: https://doi.org/10.34740/KAGGLE/DSV/9712788.
K. Maharana, S. Mondal, and B. Nemade (2022) A review: Data pre-processing and data augmentation techniques, Global Transitions Proceedings, Publisher, vol. 3, no. 1, pp. 91-99.
Q. Zhou and B. Sun (2024) Adaptive K-means clustering based under-sampling methods to solve the class imbalance problem, Data and Information Management, Publisher, vol. 8, no. 3, p. 100064.
E. S. M. El-Kenawy, S. Mirjalili, F. Alassery, Y. D. Zhang, M. M. Eid, S. Y. El-Mashad, and A. A. Abdelhamid (2022) Novel meta-heuristic algorithm for feature selection, unconstrained functions and engineering problems, IEEE Access, Publisher, vol. 10, pp. 40536-40555.
T. Verdonck, B. Baesens, M. Óskarsdóttir, and S. vanden Broucke (2024) Special issue on feature engineering editorial, Machine Learning, Publisher, vol. 113, no. 7, pp. 3917-3928.
P. Yadav, N. Menon, V. Ravi, S. Vishvanathan, and T. D. Pham (2022) EfficientNet convolutional neural networks-based Android malware detection, Computers & Security, Publisher, vol. 115, p. 102622.
B. Koonce and B. Koonce (2021) EfficientNet: Convolutional neural networks with swift for Tensorflow: image recognition and dataset categorization, Publisher, pp. 109-123.
C. Wei, S. Ren, K. Guo, H. Hu, and J. Liang (2023) High-resolution Swin transformer for automatic medical image segmentation, Sensors, Publisher, vol. 23, no. 7, p. 3420.
DOI: https://doi.org/10.31449/inf.v49i18.7865
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