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An advanced hybrid deep learning model for accurate energy load prediction in smart building
- Source :
- Energy Exploration & Exploitation, Vol 42 (2024)
- Publication Year :
- 2024
- Publisher :
- SAGE Publishing, 2024.
-
Abstract
- In smart cities, sustainable development depends on energy load prediction since it directs utilities in effectively planning, distributing and generating energy. This work presents a novel hybrid deep learning model including components of the Improved-convolutional neural network (CNN), bidirectional long short-term memory (Bi-LSTM), Graph neural network (GNN), Transformer and Fusion Layer architectures for precise energy load forecasting. Better feature extraction results from the Improved-CNN's dilated convolution and residual block accommodation of wide receptive fields reduced the vanishing gradient problem. By capturing temporal links in both directions, Bi-LSTM networks help to better grasp complicated energy use patterns. Graph neural networks improve predictive capacities across linked systems by characterizing the spatial relationships between energy-consuming units in smart cities. Emphasizing critical trends to guarantee reliable forecasts, transformer models use attention methods to manage long-term dependencies in energy consumption data. Combining CNN, Bi-LSTM, Transformer and GNN component predictions in a Fusion Layer synthesizes numerous data representations to increase accuracy. With Root Mean Square Error of 5.7532 Wh, Mean Absolute Percentage Error of 3.5001%, Mean Absolute Error of 6.7532 Wh and R 2 of 0.9701, the hybrid model fared better than other models on the ‘Electric Power Consumption’ Kaggle dataset. This work develops a realistic model that helps informed decision-making and enhances energy efficiency techniques, promoting energy load forecasting in smart cities.
Details
- Language :
- English
- ISSN :
- 01445987 and 20484054
- Volume :
- 42
- Database :
- Directory of Open Access Journals
- Journal :
- Energy Exploration & Exploitation
- Publication Type :
- Academic Journal
- Accession number :
- edsdoj.37117d05f4834b6486a6c3c4a4d51e0c
- Document Type :
- article
- Full Text :
- https://doi.org/10.1177/01445987241267822