Forecasting Global Building Energy Dynamics under Socioeconomic Transitions: An XGBoost-Based Approach
Accurate long-term forecasting of building energy demand is a cornerstone for achieving sustainable climate mitigation targets. However, traditional modeling frameworks often rely on static assumptions of internal heat gains, neglecting the evolving socioeconomic drivers that shape global energy patterns. This research bridges that gap by introducing a dynamic, data-driven approach to forecast internal heat gains using the XGBoost ensemble model. By integrating historical data with future projections aligned with the Shared Socioeconomic Pathways (SSPs), the study provides a comprehensive, globally consistent dataset that enhances predictive accuracy and supports climate-resilient building design strategies.
Dynamic Modeling of Internal Heat Gains
The study leverages advanced machine learning, specifically XGBoost, to model and forecast key variables influencing internal heat gains such as household size and energy intensity across major end-uses. Unlike conventional regression-based models, XGBoost captures nonlinear interactions between socioeconomic indicators and energy behavior, improving both interpretability and performance. The framework integrates country-level predictors—income, urbanization, and population—to simulate dynamic variations over time. This approach marks a significant shift from static estimations toward a more flexible, data-rich paradigm suited for the complexities of global energy forecasting.
Integration of Shared Socioeconomic Pathways (SSPs)
A central component of the research lies in embedding the Shared Socioeconomic Pathways (SSPs) as future scenario drivers. Each SSP represents a distinct developmental trajectory, allowing the model to forecast energy-related variables under different economic and demographic futures. This integration ensures that forecasts are not just statistically robust but also contextually relevant to policy and sustainability objectives. The SSP-driven approach offers valuable insights into how global socioeconomic evolution may reshape building energy dynamics through 2100, enabling more adaptive and future-oriented energy modeling.
Model Validation and Global Performance Assessment
The robustness of the forecasting framework was validated against a hold-out set of historical data, achieving a high global coefficient of determination (R² ≥ 0.96). This strong predictive performance underscores the model’s reliability while also revealing regional limitations due to data scarcity. Such high accuracy, combined with transparency in methodological assumptions, enhances confidence in long-term projections. The validation step also serves as a methodological benchmark for future studies aiming to integrate socioeconomic dynamics into large-scale energy modeling.
Application in Building Energy Simulations
The predictive outputs were further tested through simulation experiments involving a prototype multi-apartment building across diverse climatic and socioeconomic regions. Results showed substantial deviations when dynamic heat gains replaced traditional static inputs—up to 30 % higher energy demand in developing regions and up to 27 % lower in developed nations. These findings reveal a transformative implication: static assumptions can significantly misrepresent energy balance, potentially leading to inefficient design and policy decisions. Thus, the adoption of dynamic modeling offers a more accurate lens for understanding global energy transitions in the built environment.
Implications for Policy and Future Research
This research advocates for a paradigm shift in global building codes, energy models, and policy frameworks toward incorporating dynamic, context-specific heat gain assumptions. By providing an open-access dataset and methodological roadmap, it empowers researchers, policymakers, and engineers to design buildings and systems that are resilient to socioeconomic uncertainties. Future studies can build upon this foundation to explore region-specific adaptations, hybrid modeling approaches, and integration with climate-resilient urban planning. Ultimately, this study reinforces the importance of bridging data-driven foresight with sustainable building energy policy.
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