Efficient Waste Services in High-rise Buildings Leveraging LSTM Time Series Forecasting and IoT Automation

The preservation of natural resources in the environment depends on effective waste management in high-rise buildings. Enhancing waste services in such buildings is the focus of this research, which presents a groundbreaking technique that integrates Internet of Things (IoT) automation with Long Short-Term Memory (LSTM) time series forecasting. By analyzing data on trash creation in the past, the LSTM model can accurately forecast how much garbage will be generated in the future. IoT sensors are embedded in waste cans to track when they are full and send that information to an advanced management system. This system seamlessly combines LSTM forecasting with IoT automation to provide real-time garbage collection schedule adjustments that are influenced by both present and expected demand. Waste management operating costs and energy usage may be significantly reduced with this proactive strategy, which optimizes collection routes and avoids wasteful collections. The system ensures effective resource usage and allows for prompt interventions with real-time monitoring and alarms enabled by IoT technology. Traditional waste collection systems may not be feasible or efficient in high-rise buildings, which present their own set of problems compared to the suggested solution. The system provides a sustainable and cost-effective waste management approach that is customized to the unique demands of high-rise settings via the integration of advanced forecasting algorithms and IoT automation. This technique improves the quality of life for people while contributing to the sustainability objectives of urban development.