Based on PCA that Affect China's Energy Efficiency

2013 ◽  
Vol 401-403 ◽  
pp. 2209-2212
Author(s):  
Jin Yu Tian ◽  
Na Zhao
Keyword(s):  
Principal Component Analysis ◽  
Energy Efficiency ◽  
Energy Consumption ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Consumption Structure

Energy consumption scales have been expanding, but energy efficiency is relatively low, and its important to analysis of essential factors that affect China's energy efficiency. In this paper, via principal component analysis, make the overall comparison and evaluation on the situation of China's energy efficiency. Changes in the industrial structure and energy consumption structure are the most important factor affecting energy efficiency.

scholarly journals Prediction of China’s Energy Consumption Based on Robust Principal Component Analysis and PSO-LSSVM Optimized by the Tabu Search Algorithm

Energies ◽  
2019 ◽  
Vol 12 (1) ◽  
pp. 196 ◽  
Author(s):  
Lihui Zhang ◽  
Riletu Ge ◽  
Jianxue Chai
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Tabu Search ◽  
Industrial Structure ◽  
Principal Component ◽  
Component Analysis ◽  
Support Vector ◽  
Forecasting Model ◽  
Robust Principal Component Analysis ◽  
Consumption Structure

China’s energy consumption issues are closely associated with global climate issues, and the scale of energy consumption, peak energy consumption, and consumption investment are all the focus of national attention. In order to forecast the amount of energy consumption of China accurately, this article selected GDP, population, industrial structure and energy consumption structure, energy intensity, total imports and exports, fixed asset investment, energy efficiency, urbanization, the level of consumption, and fixed investment in the energy industry as a preliminary set of factors; Secondly, we corrected the traditional principal component analysis (PCA) algorithm from the perspective of eliminating “bad points” and then judged a “bad spot” sample based on signal reconstruction ideas. Based on the above content, we put forward a robust principal component analysis (RPCA) algorithm and chose the first five principal components as main factors affecting energy consumption, including: GDP, population, industrial structure and energy consumption structure, urbanization; Then, we applied the Tabu search (TS) algorithm to the least square to support vector machine (LSSVM) optimized by the particle swarm optimization (PSO) algorithm to forecast China’s energy consumption. We collected data from 1996 to 2010 as a training set and from 2010 to 2016 as the test set. For easy comparison, the sample data was input into the LSSVM algorithm and the PSO-LSSVM algorithm at the same time. We used statistical indicators including goodness of fit determination coefficient (R2), the root means square error (RMSE), and the mean radial error (MRE) to compare the training results of the three forecasting models, which demonstrated that the proposed TS-PSO-LSSVM forecasting model had higher prediction accuracy, generalization ability, and higher training speed. Finally, the TS-PSO-LSSVM forecasting model was applied to forecast the energy consumption of China from 2017 to 2030. According to predictions, we found that China shows a gradual increase in energy consumption trends from 2017 to 2030 and will breakthrough 6000 million tons in 2030. However, the growth rate is gradually tightening and China’s energy consumption economy will transfer to a state of diminishing returns around 2026, which guides China to put more emphasis on the field of energy investment.

Research on Energy Efficiency and Environmental Pollution in China: a Simultaneous Equations Approach

2011 ◽  
Vol 361-363 ◽  
pp. 974-977 ◽  
Author(s):  
Ying Nan Dong ◽  
Yu Duo Lu ◽  
Jiao Jiao Yu
Keyword(s):  
Energy Efficiency ◽  
Sustainable Development ◽  
Energy Consumption ◽  
Environmental Pollution ◽  
Energy Intensity ◽  
Industrial Structure ◽  
Economic Loss ◽  
Simultaneous Equations ◽  
Consumption Structure ◽  
The Relationship

This paper examined the relationship between the energy efficiency and the environmental pollution. By using the data of energy intensity and economic loss caused by environmental pollution (ELP) in China from 1989-2009, a simultaneous equations was developed. The result of two-stage OLS estimation suggested that the energy had exerted positive influences on the decreasing of the environmental pollutions. By enhancing the energy efficiency and adjusting the industrial structure and energy consumption structure, China is exploring a road for sustainable development in the energy conservation.

Effects of Energy Prices on Energy Efficiency in China

2014 ◽  
Vol 962-965 ◽  
pp. 1767-1772
Author(s):  
Zun Ming Ren
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Industrial Structure ◽  
Time Series Data ◽  
Series Data ◽  
Energy Prices ◽  
Electricity Prices ◽  
Consumption Structure ◽  
Integration Test

The paper utilized the co-integration test, error correction model and Granger causality test, and other methods to verify the influence of the coal, oil and electricity prices, industrial and energy consumption structures on China's energy efficiency based on time-series data from 1979 to 2010. Test results show that: there is long-term equilibrium relationship of the energy prices, industrial structure, energy consumption structure and energy efficiency; coal prices, industrial structure and energy consumption structure are the Granger reasons of energy efficiency both in the short and long run; while the oil and electricity prices only constitute the long-term Granger reasons of energy efficiency. Finally, it analyzed the implications of policies of the empirical results and provided some constructive suggestions.

scholarly journals Ranking of energy consumption objects using the principal components method

2020 ◽  
Vol 1 (57) ◽  
pp. 39-44
Author(s):  
A. Perekrest ◽  
V. Ogar ◽  
О. Vovna ◽  
M. Kushch-Zhyrko
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Correlation Analysis ◽  
Principal Components ◽  
Initial Conditions ◽  
Principal Component ◽  
Component Analysis ◽  
Energy Resources ◽  
Technical Documentation ◽  
Homogeneous Groups

Ensuring comfortable conditions in civil buildings requires the implementation of tasks of monitoring and forecasting the cost of energy resources, as well as energy-efficient management of heating engineering systems and its equipment. The implementation of appropriate automation and monitoring solutions allows the accumulation of a significant amount of data. To increase the informativeness of the analysis of energy efficiency in the operation of civil buildings a model of their information ranking was developed using correlation analysis and the principal component analysis. Based on the interdisciplinary methodology of data analysis (CRISP-DM), the basic indicators were determined for the accepted initial conditions on electricity and heat consumption of the university buildings and the matrix of correlation coefficients of their interrelation was estimated. Certain data (external volume and area of the building and average temperature values for this region according to the norm) are obtained from the technical documentation of buildings and available from open sources, others (amount of consumed heat and electricity, indoor temperature) are determined during operation and characterize the efficiency of energy resources in the building. At the initial stage, a correlation analysis of the relationship between the main parameters that characterize buildings and their consumption of energy resources. The principal component analysis was used to reduce the dimensionality of the feature set of data and to identify homogeneous groups of energy consumption objects. The obtained four components explain about 90% of the variance of the initial data and characterize the efficiency of energy use in terms of temperature, volume and coefficient of heating degree days of the heating season. The obtained results are recommended for implementation in modern systems of energy monitoring and municipal energy management as applied models for diagnosing abnormal situations and sound management decisions. Keywords – buildings; energy consumption; principal components; machine learning; data segmentation.

A Lifecycle Sustainability Assessment of CO2 Emissions, Energy Consumption and Social Aspects of Methylic and Ethylic Biodiesel Using Principal Component Analysis

2019 ◽  
Vol 965 ◽  
pp. 1-12
Author(s):  
Stefano Ferrari Interlenghi ◽  
José Luiz de Medeiros ◽  
Ofélia de Queiroz Fernandes Araújo
Keyword(s):  
Principal Component Analysis ◽  
Energy Consumption ◽  
Energy Loss ◽  
Principal Component ◽  
Component Analysis ◽  
Environmental Indicators ◽  
Biodiesel Production ◽  
Social Aspects ◽  
Carbon Intensity ◽  
Production Chain

The possibility of using renewable feedstocks for biodiesel production and reducing gas emissions makes it an attractive large-scale substitute to traditional fossil diesel. Although renewability is one of the main driving forces in biodiesel use, traditional production routes employ methanol as the transesterification agent, a chemical generated from fossil carbon. Aiming at further improving biodiesel’s sustainable performance, the replacement of methanol by ethanol has been proposed. Use of the ethylic production route could further reduce CO2 emissions, energy consumption and generate more jobs. The objective of this study is to unveil whether substituting methanol for ethanol does indeed result in a less carbon and energy intensive production chain while also increasing job generation and decreasing social strife. To assess production chain performance a lifecycle approach was used composed by: (i) Data assemblage from literature to represent the ethylic/methylic biodiesel systems; (ii) Construction of quantitative indicators to compare material and energetic flows; and (iii) Principal Component Analysis (PCA) for data interpretation and relevance ranking of calculated social/environmental indicators. Focus was given to CO2 emissions, energy consumption and social aspects of sustainability. Results show that use of ethanol does indeed reduce CO2 emissions, due to extra agricultural carbon sinks in the production chain but increases energy consumption and energy loss. Methanol also resulted in a chain with higher average wages, more jobs generated and less forced labor cases but with a higher accident rate and a high salary disparity. PCA showed that carbon intensity is one of the most important environmental metrics while energy consumption was considered secondary, but the high correlation between these aspects highly impact chain sustainability. PCA also greatly differentiated agricultural and industrial links of respective production chains, with industrial links being governed by CO2 emissions and process safety and agricultural links by water consumption, land use and energy loss. A distinct tradeoff was seen between environmental and social considerations of sustainability and between carbon intensity and energy consumption reductions. As a result, substitution is only justified in scenarios in which CO2 emissions outweigh energy intensity and social aspects.

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