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Global warming and natural disasters have wreaked havoc on Earth these days. Energy conservation has been a priority under social development pressure, especially in the building sector. Energy consumption prediction serves as a vital tool for effective building energy management, guiding energy policy making and services distribution. However, despite the application of advanced technologies, achieving accurate energy consumption prediction still faces challenges due to potential influencing factors. This study aims to explore previously unidentified factors to enhance building energy consumption prediction accuracy. An extended life cycle energy boundary of buildings including embodied, operational, and mobile energies is proposed. To optimize the prediction processes, a hybrid Life Cycle Assessment (LCA) approach and a non-linear Bidirectional Long Short-Term Memory (Bi-LSTM) model are combined. An empirical study was conducted to predict the life cycle energy consumption of urban residential buildings in China by 2035. Results show: (1) life cycle energy consumption increased dramatically from 195.837 (±11.77) Mtce in 2000 to 1151.69 (±80.38) Mtce in 2022, but then achieved a slow decline to 796.998 ((±51.46)) Mtce by 2035; (2) there were four significant development stages in the residential field: rapid growth (2000–2010), sharp growth (2010–2018), slow growth (2018–2022), and stable fall (2022–2035); (3) mobile energy related to building location accounted for up to 28% of total energy consumption, and daily commuting is its largest emitter. However, calculation errors caused by diverse data sources and the fundamental model itself need to be addressed later. This study provides an original pathway of combining LCA with a non-linear model to improve energy consumption prediction and the completed primary historical data to help local governments' energy decision-making. [ABSTRACT FROM AUTHOR]

Rapid urbanization and rising living standards in China have significantly increased building energy consumption, creating environmental pressures that necessitate the implementation of effective long-term energy-saving policies and plans. High-quality energy assessment plays a crucial role in developing these strategies. However, accurately assessing energy consumption on a national scale can be challenging due to the complex nature of energy systems and numerous potential influencing factors. This study aims to explore the internal interaction mechanism among energy-related factors from a macro perspective to improve the accuracy of energy consumption assessment. A national-level Integrated Dynamic Energy Assessment (IDEA) model is developed to reveal the potential linear and non-linear relationships in the macro energy system through system dynamics and the least squares method, and meanwhile to quantify the life cycle energy consumption of urban residential buildings in China from 2000 to 2020 accordingly. Results show 1) the five energy-related subsystems (population, economy, building, energy, and environment) achieved significant growth at least twice. 2) there are strong connections among them, as evidenced by multivariate linear and non-linear regression equations; 3) life cycle energy consumption of buildings has increased significantly over the last 20 years from 192.86 M tce to 1131.07 M tee, and the largest contributor is operational energy (45%), followed by embodied energy (33%) and mobile energy (22%) in 2020. 4) The discrepancies in end-uses of the operational energy indicate marked changes in occupants' energy-use behaviours in recent years. This study provides not only a more precise interactive system for evaluating national-level energy consumption, but also detailed life cycle energy consumption and internal interconnection relationships that can assist China's energy policymaking and serve as a reference for other nations. [ABSTRACT FROM AUTHOR]