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Smart city article details

Title	Deep Learning Method To Analyze The Bi-Lstm Model For Energy Consumption Forecasting In Smart Cities
ID_Doc	17905
Authors	Balasubramaniyan S.; Kumar P.K.; Vaigundamoorthi M.; Rahuman A.K.; Solaimalai G.; Sathish T.; Vidhya R.G.
Year	2023
Published	International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings
DOI	http://dx.doi.org/10.1109/ICSCNA58489.2023.10370467
Abstract	Smart cities and IoT solutions are improving urban efficiency, resource optimization, and public safety by using modern technologies. Deep residual Bi-LSTM (Long Short-Term Memory) models can analyze and forecast complicated and time-varying data. This study examines how the deep residual Bi-LS TM model might improve smart city and IoT solutions. The model has broad use since it captures long-term interdependence and extracts meaningful representations from sequential data. Traffic prediction, energy consumption forecasting, environmental monitoring, predictive maintenance, public safety, and emergency response are discussed. The deep residual Bi-LSTM model provides realtime insights, accurate forecasts, and quick data processing to improve smart city systems and IoT solutions, making cities more sustainable, efficient, and secure. © 2023 IEEE.
Author Keywords	Bidirectional Long Short-Term Memory (Bi-LS TM); Internet of Things (IoT); Optimization

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