TY - GEN
T1 - Deep Learning Method to Analyze the Bi-LSTM Model for Energy Consumption Forecasting in Smart Cities
AU - Balasubramaniyan, S.
AU - Kumar, P. K.
AU - Vaigundamoorthi, M.
AU - Rahuman, A. Kaleel
AU - Solaimalai, Gautam
AU - Sathish, T.
AU - Vidhya, R. G.
N1 - Publisher Copyright:
© 2023 IEEE.
PY - 2023
Y1 - 2023
N2 - 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.
AB - 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.
KW - Bidirectional Long Short-Term Memory (Bi-LS TM)
KW - Internet of Things (IoT)
KW - Optimization
UR - http://www.scopus.com/inward/record.url?scp=85183034569&partnerID=8YFLogxK
U2 - 10.1109/ICSCNA58489.2023.10370467
DO - 10.1109/ICSCNA58489.2023.10370467
M3 - Conference contribution
AN - SCOPUS:85183034569
T3 - International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings
SP - 870
EP - 876
BT - International Conference on Sustainable Communication Networks and Application, ICSCNA 2023 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2023 International Conference on Sustainable Communication Networks and Application, ICSCNA 2023
Y2 - 15 November 2023 through 17 November 2023
ER -
