scholarly journals Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques

Energies ◽  
2017 ◽  
Vol 10 (7) ◽  
pp. 1039 ◽  
Author(s):  
Jin-Peng Liu ◽  
Qian-Ru Yang ◽  
Lin He
Keyword(s):  
Energy Efficiency ◽  
Multiple Regression ◽  
Thermal Power ◽  
Power Industry ◽  
Regression Techniques

Energy Efficiency Evaluation for Steam Turbine and Auxiliary System

2014 ◽  
Vol 472 ◽  
pp. 1017-1021
Author(s):  
Yu Bo Wang ◽  
Jing Liu ◽  
Ping Zhu ◽  
Cheng Bing He
Keyword(s):  
Energy Efficiency ◽  
Power Plant ◽  
Steam Turbine ◽  
Evaluation System ◽  
Thermal Power ◽  
Evaluation Process ◽  
Power Industry ◽  
Auxiliary System ◽  
Efficiency Evaluation ◽  
Energy Efficiency Evaluation

Thermal power industry in China is facing energyshortage , but now there is a lack of a comprehensive energy efficiency evaluation system to promote the energy efficiency ,The energy efficiency evaluation for steam turbine and auxiliary system fills this gap. On account of the fuzziness and randomness of the evaluation process, we propose the AHM-F combination evaluation method.It not only evaluted the energy efficiency level of the steam turbine and auxiliary system,but also point out the direction for further improving of energy efficiency level. The scientificalness and effectiveness of the proposed method is verfied by the example analysis of the 600MW unit in a power plant.

scholarly journals Total Factor Energy Efficiency of China’s Thermal Power Industry

2022 ◽  
Vol 14 (1) ◽  
pp. 504
Author(s):  
Ying Feng ◽  
Ching-Cheng Lu ◽  
I-Fang Lin ◽  
An-Chi Yang ◽  
Po-Chun Lin
Keyword(s):  
Power Generation ◽  
Energy Efficiency ◽  
Regional Differences ◽  
Power Efficiency ◽  
Alternative Energy ◽  
Thermal Power ◽  
Power Industry ◽  
The Sustainable Development ◽  
Thermal Power Generation ◽  
The Government

Coal-based thermal power generation has long been the main source of power generation in the mainland of China. The efficiency of power generation is an important factor that determines the energy conservation and emission reduction as well as the sustainable development of the power industry in China. By comparing the regional differences of 30 provinces in the mainland from 2013 to 2017, this study uses the Super-DDF model and the TFEE to comprehensively evaluate the energy efficiency of thermal power generation. Empirical results: Overall efficiency: eastern efficiency (1.181) is the highest, followed by western (0.956), central (0.951) and northeastern (0.926). Total factor energy efficiency: eastern efficiency (0.923) is the highest, followed by western (0.754), central (0.742) and northeastern (0.710). The government and power industry managers should fully consider the regional differences in the field of thermal power generation when formulating policies so as to improve the power efficiency and promote the green development of power industry in China. Based on the analysis results, although the coal-fired power industry is more mature than other alternative energy industries, the expansion of thermal power generation cannot be considered if CO2 emissions are to be reduced. Additionally, the market share and competitiveness of the local power industry can be increased based on the different conditions of the resource endowments of each region.

scholarly journals Problems polyene innovation activities of enterprises heat supply in the region

2018 ◽  
Vol 11 (2) ◽  
pp. 169-176
Author(s):  
T. B. Malkova ◽  
A. V. Malkov
Keyword(s):  
Energy Efficiency ◽  
Heat Supply ◽  
Thermal Power ◽  
Balance Sheet ◽  
Power Industry ◽  
Innovative Activity ◽  
Process Equipment ◽  
Limiting Factor ◽  
Heat Power ◽  
Industrial Enterprises

The paper considers the main trends and ways to improve the innovative activity of enterprises of heat supply in theIvanovoregion. The main problems hindering the dynamic development of innovative activity of enterprises of thermal power industry in the region are considered. The hierarchy of the basic levels of infrastructure of heat power engineering of economic system is offered. For each level of heat power infrastructure of the region certain tasks providing the necessary level of efficiency of functioning of system have to be solved. One of the most important tasks of thermal power enterprises is the introduction of energy saving and innovative technologies, energy efficiency, analysis of the balance sheet structure, economically justified tariffs and the introduction of new mechanisms for the implementation of innovation and investment programs. The key objectives and principles for the effective functioning of the infrastructure of the power system. The key tasks of thermal power companies are their technical controlling and audit, allowing timely detection of defective areas, reducing energy efficiency in the industry and creating the possibility of risk situations that can cause damage to the organization. A limiting factor in the interactions between the elements of the infrastructure are the consumers of heat energy, which include industrial enterprises and enterprises of sphere of services, social services, state enterprises, marketing and management companies, private persons etc. An important condition for solving many of these problems in the infrastructure of heat power industry in the region is to improve the efficiency of its innovation and increase the responsibility of management for the state of process equipment and heating networks. The most important component of this increase is the formation of an innovative environment, as a result of which there is a transformation of the methods of organizing and managing the infrastructure of the heat power industry of the region, an update of the technological base, the introduction of advanced technologies in production and management is achieved.

scholarly journals Energy efficiency evaluation of oil producing economies in Africa: DEA, malmquist and multiple regression approaches

2021 ◽  
Vol 2 ◽  
pp. 100025
Author(s):  
Mark Awe Tachega ◽  
Xilong Yao ◽  
Yang Liu ◽  
Dulal Ahmed ◽  
Hui Li ◽  
...  
Keyword(s):  
Energy Efficiency ◽  
Multiple Regression ◽  
Efficiency Evaluation ◽  
Energy Efficiency Evaluation

Some issues in the simulation of ANOVA by multiple regression techniques

1975 ◽  
Vol 5 (1) ◽  
pp. 199-207 ◽  
Author(s):  
John T. McArthur ◽  
Barry J. Fraser ◽  
Leo H. T. West
Keyword(s):  
Multiple Regression ◽  
Regression Techniques

scholarly journals The optimal number of partial least squares components in genomic selection for pork pH

2017 ◽  
Vol 47 (1) ◽  
Author(s):  
Fernanda Gomes da Silveira ◽  
Darlene Ana Souza Duarte ◽  
Lucas Monteiro Chaves ◽  
Fabyano Fonseca e Silva ◽  
Ivan Carvalho Filho ◽  
...  
Keyword(s):  
Least Squares ◽  
Genomic Selection ◽  
Multiple Regression ◽  
Partial Least Squares ◽  
Predictive Accuracy ◽  
Principal Component ◽  
Theoretical Background ◽  
Optimal Number ◽  
Number Of Components ◽  
Regression Techniques

ABSTRACT: The main application of genomic selection (GS) is the early identification of genetically superior animals for traits difficult-to-measure or lately evaluated, such as meat pH (measured after slaughter). Because the number of markers in GS is generally larger than the number of genotyped animals and these markers are highly correlated owing to linkage disequilibrium, statistical methods based on dimensionality reduction have been proposed. Among them, the partial least squares (PLS) technique stands out, because of its simplicity and high predictive accuracy. However, choosing the optimal number of components remains a relevant issue for PLS applications. Thus, we applied PLS (and principal component and traditional multiple regression) techniques to GS for pork pH traits (with pH measured at 45min and 24h after slaughter) and also identified the optimal number of PLS components based on the degree-of-freedom (DoF) and cross-validation (CV) methods. The PLS method out performs the principal component and traditional multiple regression techniques, enabling satisfactory predictions for pork pH traits using only genotypic data (low-density SNP panel). Furthermore, the SNP marker estimates from PLS revealed a relevant region on chromosome 4, which may affect these traits. The DoF and CV methods showed similar results for determining the optimal number of components in PLS analysis; thus, from the statistical viewpoint, the DoF method should be preferred because of its theoretical background (based on the "statistical information theory"), whereas CV is an empirical method based on computational effort.

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