Life-cycle energy consumption prediction of urban residential buildings in China adopting the transformed rebound model

Abstract

Excessive building energy consumption is a global concern, and as one of its largest contributors, China is facing significant challenges in improving energy assessments and urbanisation. Accurately predicting the energy consumption of urban residential buildings is vital for updating energy conservation strategies and achieving sustainable development goals. A systematic review of 417 relevant articles from the ‘Web of Science’ database revealed substantial discrepancies between actual and predicted building energy consumptions. Further qualitative analysis identified gaps in the typical four-stage prediction procedure that involves energy scope, input features, data collection, and a prediction model. To address these gaps, this study aims to more accurately predict the national-scale lifecycle energy consumption of urban residential buildings in China by 2035 using a transformed rebound prediction model. This model includes three dynamic parameters measured using four designed objectives. The three parameters include benchmark energy consumption values without any new intervention (𝐸_b,y ), expected energy consumption values with various advanced technologies and measures (𝐸_e,y ), and technical advance-based energy rebound effects (d_T,y). The key findings are as follows: 1) The life-cycle energy consumption of buildings is expected to peak at 1304.16 Mtce in 2032. 2) Although the ideal energy savings are expected to reach 206.01 Mtce in 2035, the actual savings are estimated to be 150.15 Mtce because of the energy rebound effect. 3) There are four energy development stages from 2000–2035, including rapid growth, fluctuations, slow growth, and slow decline. 4) Operational energy consumption is expected to dominate, far exceeding embodied and mobile energy consumptions. 5) Embodied energy production, living electricity, lost energy during raw energy processing, daily commuting, and natural gas usage are identified as the major contributors to energy consumption. This research broadens the conventional scope of building life-cycle energy by incorporating mobile energy related to the building materials and residential transportation caused by building layout and surrounding environment, offering a comprehensive life-cycle analysis and systematic approach. The transformed rebound model and its prediction process provide a precise systemic improvement in prediction accuracy from multiple perspectives. The outcomes are expected to aid in updating energy-saving policies and strategies, helping realise sustainable development goals locally and globally. The model can be scaled and applied to other regions worldwide.

Date of Thesis submission	2024
Original language	English
Awarding Institution	Western Sydney University
Supervisor	Vivian Tam (Supervisor) & Khoa Le (Supervisor)