A novel approach to dynamic life cycle assessment: Integrating climate change and cooling operation patterns in building energy consumption forecasting

Abstract

Given a building’s extended lifespan, Dynamic Life Cycle Assessment (DLCA) has become essential for accurately quantifying its operational energy impacts. However, developing a Dynamic Life Cycle Inventory (DLCI) for buildings presents substantial challenges, particularly in data selection, abstraction, and future energy flow projections. This study introduces an integrated approach to DLCI, incorporating multiple construction techniques, cooling operation patterns, and climate change projections. A parametric simulation was conducted in EnergyPlus™, evaluating 432 variations in construction parameters in a Brazilian social housing unit, including different roof, wall, and window materials, and spatial orientation. Five cooling operation scenarios were defined, ranging from split-off (0 h/day) to continuous operation (24 h/day). The resulting energy consumption dataset was used to train an Artificial Neural Network (ANN), while a linear regression model projected external temperature over the next 50 years. The trained ANN then estimated future building energy consumption, incorporating projected climate conditions. The findings indicate that, in the continuous cooling operation scenario (24 h/day), the total electricity consumption for artificial cooling may reach 18,000 kWh/m2 over the building’s lifetime, underscoring the long-term energy demand in Brazilian social housing. Additionally, the sensitivity analysis revealed that each 1 °C rise in external temperature leads to an approximately 8 % increase in cooling energy consumption, reinforcing the need for adaptive design strategies to mitigate future energy demands. The proposed approach provides a scalable and data-driven framework for building energy forecasting, offering valuable insights for sustainable design and climate policy development, particularly in social housing contexts.