Deep Learning Method to Analyze the Bi-LSTM Model for Energy Consumption Forecasting in Smart Cities

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.