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Smart city article details

Title	An Advanced Hybrid Deep Learning Model For Accurate Energy Load Prediction In Smart Building
ID_Doc	7417
Authors	Sunder R.; R S.; Paul V.; Punia S.K.; Konduri B.; Nabilal K.V.; Lilhore U.K.; Lohani T.K.; Ghith E.; Tlija M.
Year	2024
Published	Energy Exploration and Exploitation, 42, 6
DOI	http://dx.doi.org/10.1177/01445987241267822
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 R2 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. © The Author(s) 2024.
Author Keywords	Bi-LSTM; deep learning; Energy load prediction; GNN; improved-CNN; sustainable development

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