Predictive Modelling of Service Building and Users Mobility Related Energy Consumption Through Machine Learning and Graph Based Analysis

ABSTRACT Accurately assessing the energy consumption associated with public buildings and the services they provide is essential for supporting sustainable infrastructure planning. This work presents an integrated modelling framework that combines machine learning prediction of building energy use with a graph based representation of user mobility, allowing a comprehensive estimation of total energy demand. The approach includes building operations, service related energy, travel by users and staff and supply chain logistics. Building energy consumption is predicted through supervised models trained on a curated subset of commercial facilities selected for their similarity to public service environments. Mobility is modelled using a synthetic geographical network that encodes population distribution, available transportation modes, behavioural tendencies and relocation dynamics. The framework is applied to a university reorganization scenario, exploring alternative facility configurations and varying degrees of remote activity. Results indicate that user travel is generally the dominant contributor to total energy demand, while the importance of building energy increases as in person attendance decreases. Sensitivity analyses confirm the robustness of the optimal configurations under different behavioural assumptions. We stress that this is not actually an optimization algorithm, but a parameter sweep; in real situations, constraints may be applied, for instance for building availability, costs, opportunity of sharing services or specific goals. The methodology is further demonstrated in a real healthcare application, where the predictive model enables reliable estimation of building energy use in the absence of direct measurements. Overall, the proposed framework illustrates how data driven modelling and intelligent system techniques can support sustainable decision making for complex public service infrastructures.