Sustainable Computing and Communication Technologies for Digital Twin–Based Energy Optimization in Smart Infrastructure Systems

Abstract

There is a tremendous rise in energy usage when smart infrastructure systems are being rolled out, hence the need to develop sustainable computing and communication technologies that will ensure the proper usage of the available resources. To provide real‐time energy management in smart infrastructure landscape, this paper suggests an energy optimization framework, based on a digital twin, which involves Internet of Things (IoT), edge computing and intelligent modeling. It uses the UCI Energy Efficiency Dataset to simulate the behavior of buildings in terms of energy consumption and builds a data‐driven digital twin to optimize the building behavior dynamically. The suggested strategy will decrease the overall power usage by 245.6 to 168.3 kWh which will result in an enhancement of 31.5%. Also, the overall accuracy of prediction is increased, the root mean square error (RMSE) is decreased to 3.87 and the mean absolute error (MAE) is decreased to 2.76 that are improved by 39.7 or 44.2, respectively. The architecture can be shown to perform efficiently with real‐time performance with processing latency of 5–8 ms and end‐to‐end latency of 12–18 ms, and less communication overheads of 6–9 times through edge‐based processing. These findings embrace the usefulness of the suggested digital twin‐based sustainable computing model in enhancing energy conservation, lowering operational expenses by enabling scalable systems of smart infrastructure.