A Multi-temporal Deep Learning Architecture for Smart Retrofitting of Buildings in Service

Given the numerous studies on how to make buildings smarter through smart devices, there is a need for more research on retrofitting large buildings with legacy equipment and systems. We propose a deep learning based cyber-physical control solution for smart retrofitting of buildings in service. Specifically, our solution first collects real-time operational data without interrupting the operation of the original control system, and then utilizes the LSTM-CNN prediction model to learn the multi-scene control strategy embedded in the historical data. Based on this model, intelligent retrofitting of many large buildings can be realized, thus significantly reducing the manpower required for manual operation and maintenance. The model was experimented using real data from the HVAC system of the T3 terminal building of Beijing Capital International Airport, and the results show that the proposed solution can be used in conjunction with existing equipment to accomplish the smart retrofit and can effectively replace manual control.