This study proposes a methodology for fairly assessing the building energy usage level of occupants using a public open dataset. A case study of day-care centers in South Korea was conducted to demonstrate the methodology. An open dataset of monthly building energy consumption in the day-care centers was obtained and grouped based on thermal performance (e.g., U-value). For each performance group, monthly electricity consumption (representing cooling demand), gas consumption (representing heating demand), and energy consumption were segmented using k-means clustering into heavy, medium, and light users. For each user cluster, representative monthly trajectories were ascertained by averaging the values. Using the input variables of the building performance and environmental factors, the machine learning-based evaluation models were developed to purely infer the impact of the occupants on energy consumption (monthly trajectories). All models exhibited reasonable performance (12% cv(RMSE) in the worst case); the linear regression model is recommended for its simplicity and applicability in policymaking and decision-making contexts. Finally, the efficacy of the developed model in evaluating energy usage levels is presented with an example.

Model-based predictive control provides an effective approach for improving building energy efficiency by optimizing heating, ventilation, and air-conditioning operation while maintaining indoor thermal conditions. However, its performance strongly depends on the availability of an accurate and generalizable prediction model of building thermal dynamics. Existing studies have focused on individual buildings, where each building is modeled and optimized independently. Recent studies have demonstrated the potential of federated learning (FL) for a range of building applications. However, most existing approaches remain predominantly data driven. This study proposes a federated physics-informed neural network for generalized indoor temperature prediction across multiple building zones. The global RC model was developed and then incorporated into the neural-network loss function as a physics-based constraint, while FL was employed to develop a shared global prediction model without pooling the raw datasets of individual clients. The developed global model demonstrated good single-step-ahead prediction performance across the four target zones, with root-mean squared error (RMSE) values ranging from 0.36 °C to 0.59 °C, mean absolute error values from 0.25 °C to 0.57 °C. For multi-step-ahead prediction, the average RMSE was approximately 0.68 °C during the estimation period and 0.99 °C during validation. Moreover, when the zone excluded from the federated training process and evaluated as an unseen client the proposed model achieved RMSE values of 0.74 °C and 0.86 °C for one-step-ahead and multi-step-ahead predictions, respectively, compared with 0.86 °C and 0.98 °C for the conventional data-driven FL model.