Environment driven consumer psychological behavior based MPC energy model: a multi-dimensional digital twins framework using deep learning

The paper proposes a Model Predictive Control (MPC) energy model for environment-driven Multi-Dimensional Digital Twins (MDDTs) powered by consumer psychological behaviour accomplished via Deep Learning (DL) to minimise energy consumption. This real-time integration of environmental factors, temperature, humidity, and lighting, with consumer behaviour patterns and physiological responses, provides the basis for a new integrated model for the dynamic control of energy systems. The proposed model relies on IoT sensors and real-time data aggregation in making predictions and optimising energy consumption, considering the environmental impacts on consumer comfort. The use of DL models improves MPC by uncovering non-linear correlations in the data and having the ability to predict future energy demands. The MPC architecture operates under a closed-loop operating system and, therefore, enables adjustment of real-time feedback according to the space, environmental, and consumer behaviour changes. Due to its predictive nature, MPC can make anticipatory changes to energy systems, which will save energy without compromising comfort. The proposed model is validated using extensive simulation to respond to dynamic situations with optimal energy consumption while ensuring adequate user comfort. The real-time application of multi-dimensional heterogeneous data proves the applicability and robustness of the proposed system in real-world environments.