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

Retrofitting older buildings for energy efficiency is paramount in today's sustainability and environmental awareness era. Older buildings contribute greatly to energy waste since they typically lack new energy-efficient technology. Reducing carbon emissions, lowering energy bills, and extending the life of these historic landmarks all depend on fixing the energy inefficiency that plagues these buildings. Despite advanced technologies' remarkable progress, the potential of the Internet of Things and deep learning in older buildings has not been unexplored. Major obstacles include expensive and out-of-date infrastructure and the difficulty of incorporating new technology into historically significant structures. Existing research has mostly ignored older infrastructures' unique requirements and limitations in favour of current or newly built services. In addition, comprehensive research on integrating deep learning with the Internet of Things in this specific environment has been lacking. Smart building energy management is made possible by the Internet of Things (IoT) and deep learning. Architectural limitations, outmoded infrastructure, and the necessity for non-invasive retrofitting solutions all contribute to the difficulty of energy monitoring and improvement in older buildings. This research proposes combining the IoT with Deep Learning-enhanced Predictive Energy Modeling (DL-PEM) to make an energy management system that can change and adapt to the needs of older buildings. Data from IoT sensors is collected on occupancy, temperature, lighting, and equipment usage and then analyzed using Deep Learning models to determine the most efficient energy consumption patterns. Beyond its energy- saving potential, this method has many potential uses. Spotting and fixing structural problems before they become major can improve occupant comfort, reduce maintenance costs, and pave the way for predictive maintenance. Integration with the Smart grid and demand response programs can be facilitated, too, improving the reliability of the power system as a whole. Our IoT and Deep Learning-based energy management solution optimizes energy usage, reduces expenses, and mitigates environmental impact in older buildings, as shown by extensive simulation studies. The system's performance is compared to more conventional methods, and its flexibility is evaluated in various building and usage contexts. The experimental outcomes show the suggested DLPEM model increases the energy demand forecasting analysis, energy consumption analysis, thermal comfort optimization analysis, seasonal variation analysis, and occupancy data analysis.