Title : Internet of things and deep learning-enhanced monitoring for energy efficiency in older buildings

1. Objectives
The primary objectives of this article are:
- Optimizing energy consumption: Reducing energy costs by utilizing smart systems to identify and control excessive energy usage.
- Improving energy efficiency in older buildings: Enhancing the energy performance of older buildings that lack modern infrastructure.
- Reducing environmental impact: Minimizing greenhouse gas emissions by reducing energy consumption, particularly in large and high-consumption buildings.
2. Strategies
- IoT sensors deployment: Installing smart sensors throughout the building to collect data on temperature, humidity, lighting, and energy usage.
- Data processing with deep learning: Training deep learning models on IoT-collected data to identify energy consumption patterns and make accurate energy demand predictions.
- Automated monitoring and suggestions: Enabling systems to automatically offer suggestions and actions for energy optimization.
- Remote management: Allowing remote monitoring and control of energy usage, enabling managers to make efficient decisions from anywhere.
3. Applications
- HVAC systems: Monitoring and precisely controlling heating and cooling systems to optimize energy use and reduce maintenance costs.
- Lighting management: Adjusting building lighting based on real-time needs and day-night cycles.
- Water heaters and high-energy devices: Monitoring high-energy-consuming devices to identify inefficiencies and reduce excessive usage.
- Public buildings and city energy management: Applying these methods in public buildings like hospitals, schools, and offices for optimized energy consumption.
4. Identified Patterns
- Seasonal and daily patterns: Recognizing energy consumption patterns across different times of the day and seasons to improve system settings for heating and lighting.
- Anomaly detection: Identifying unusual changes in energy usage, which could indicate equipment inefficiencies or malfunctions.
- User behavior patterns: Learning user behavior to adjust system operations for improved energy consumption.
5. Models and Techniques
- Recurrent Neural Networks (RNN): For processing sequential data to forecast future energy demands.
- Convolutional Neural Networks (CNN): In applications that involve spatial data (e.g., heat maps) to detect spatial patterns.
- Hybrid learning models: Combining different networks for improved prediction accuracy and pattern recognition.
- Reinforcement learning models: Using these models to automatically learn optimal energy management strategies over time.
6. Results
- Significant energy reduction: Experimental results show substantial reductions in energy consumption, leading to decreased operational costs.
- Increased equipment lifespan: Improved monitoring and precise control extend equipment lifespan, reducing maintenance costs.
- Reduced environmental impact: Lower energy use contributes to decreased greenhouse gas emissions, especially in older and high-consumption buildings.
- Better energy demand forecasting: Predictive models provide accurate forecasts of future energy needs, enabling timely decision-making.
7. Challenges
- Hardware and software needs: Installing and maintaining IoT sensors and networks can be challenging for older buildings.
- Initial implementation costs: The expense of sensors, control systems, and deep learning software can be high.
- Data security: Protecting data collected from IoT systems and preventing hacking and intrusion is crucial.
Summary
The study demonstrates that IoT and deep learning can significantly improve energy efficiency in older buildings. Despite implementation challenges and upfront costs, these methods can yield long-term economic savings and environmental benefits, playing a vital role in reducing carbon emissions.