Analysis of achievable residential energy-saving potential and its implications for effective policy interventions: A study of Xiamen city in southern China

2016 ◽  
Vol 62 ◽  
pp. 507-520 ◽  
Author(s):  
Fei Guo ◽  
Lado Kurdgelashvili ◽  
Magnus Bengtsson ◽  
Lewis Akenji
Energy ◽  
2018 ◽  
Vol 142 ◽  
pp. 373-383 ◽  
Author(s):  
Fei Guo ◽  
Lewis Akenji ◽  
Patrick Schroeder ◽  
Magnus Bengtsson

2020 ◽  
Vol 12 (19) ◽  
pp. 8016
Author(s):  
Feng Wang ◽  
Min Wu ◽  
Jiachen Hong

To achieve the national carbon intensity (NCI) target, China should adopt effective mitigation measures. This paper aims to examine the effects of key mitigation measures on NCI. Using the input-output table in 2017, this paper establishes the elasticity model of NCI to investigate the effects of industrial development, intermediate input coefficients, energy efficiency, and residential energy saving on NCI, and further evaluates the contributions of key measures on achieving NCI target. The results are shown as follows. First, the development of seven sectors will promote the increase of NCI while that of 21 sectors will reduce NCI. Second, NCI will decrease significantly with the descending of intermediate input coefficients of sectors, especially electricity production and supply. Third, improving energy efficiency and residential energy saving degree could reduce NCI, but the latter has limited contribution. Fourth, the development of all sectors will reduce NCI by 10.11% in 2017–2022 if sectors could continue the historical development trends. Fifth, assuming that sectors with rising intermediate input coefficients would keep their coefficients unchanged in the predicting period and sectors with descending coefficients would continue the historical descending trend, the improvement of technology and management of all sectors will reduce NCI by 14.02% in 2017–2022.


Energies ◽  
2020 ◽  
Vol 14 (1) ◽  
pp. 81
Author(s):  
Rongjiang Ma ◽  
Shen Yang ◽  
Xianlin Wang ◽  
Xi-Cheng Wang ◽  
Ming Shan ◽  
...  

Air-conditioning systems contribute the most to energy consumption among building equipment. Hence, energy saving for air-conditioning systems would be the essence of reducing building energy consumption. The conventional energy-saving diagnosis method through observation, test, and identification (OTI) has several drawbacks such as time consumption and narrow focus. To overcome these problems, this study proposed a systematic method for energy-saving diagnosis in air-conditioning systems based on data mining. The method mainly includes seven steps: (1) data collection, (2) data preprocessing, (3) recognition of variable-speed equipment, (4) recognition of system operation mode, (5) regression analysis of energy consumption data, (6) constraints analysis of system running, and (7) energy-saving potential analysis. A case study with a complicated air-conditioning system coupled with an ice storage system demonstrated the effectiveness of the proposed method. Compared with the traditional OTI method, the data-mining-based method can provide a more comprehensive analysis of energy-saving potential with less time cost, although it strongly relies on data quality in all steps and lacks flexibility for diagnosing specific equipment for energy-saving potential analysis. The results can deepen the understanding of the operating data characteristics of air-conditioning systems.


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