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

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.