An advanced hybrid deep learning model for accurate energy load prediction in smart building
| Autor: | R Sunder, Sreeraj R, Vince Paul, Sanjeev Kumar Punia, Bhagavan Konduri, Khan Vajid Nabilal, Umesh Kumar Lilhore, Tarun Kumar Lohani, Ehab Ghith, Mehdi Tlija |
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| Jazyk: | angličtina |
| Rok vydání: | 2024 |
| Předmět: | |
| Zdroj: | Energy Exploration & Exploitation, Vol 42 (2024) |
| Druh dokumentu: | article |
| ISSN: | 0144-5987 2048-4054 01445987 |
| DOI: | 10.1177/01445987241267822 |
| Popis: | 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. |
| Databáze: | Directory of Open Access Journals |
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