scholarly journals Decomposition and Forecasting of CO2 Emissions in China’s Power Sector Based on STIRPAT Model with Selected PLS Model and a Novel Hybrid PLS-Grey-Markov Model

Energies ◽  
2018 ◽  
Vol 11 (11) ◽  
pp. 2985 ◽  
Author(s):  
Herui Cui ◽  
Ruirui Wu ◽  
Tian Zhao
Keyword(s):  
Markov Model ◽  
Co2 Emissions ◽  
Ridge Regression ◽  
Back Propagation ◽  
Partial Least Square ◽  
Least Square ◽  
Low Carbon ◽  
Power Sector ◽  
Stirpat Model ◽  
Generation Structure

China faces significant challenges related to global warming caused by CO2 emissions, and the power industry is a large CO2 emitter. The decomposition and accurate forecasting of CO2 emissions in China’s power sector are thus crucial for low-carbon outcomes. This paper selects seven socio-economic and technological drivers related to the power sector, and decomposes CO2 emissions based on two models: the extended stochastic impacts by regression on population, affluence and technology (STIRPAT) model and the partial least square (PLS) model. Distinguished from previous research, our study first compares the effects of eliminating the multicollinearity of the PLS model with stepwise regression and ridge regression, finding that PLS is superior. Further, the decomposition results show the factors’ absolute elasticity coefficients are population (2.58) > line loss rate (1.112) > GDP per capita (0.669) > generation structure (0.522) > the urbanization level (0.512) > electricity intensity (0.310) > industrial structure (0.060). Meanwhile, a novel hybrid PLS-Grey-Markov model is proposed, and is verified to have better precision for the CO2 emissions of the power sector compared to the selected models, such as ridge regression-Grey-Markov, PLS-Grey-Markov, PLS-Grey and PLS-BP (Back propagation neutral network model). The forecast results suggest that CO2 emissions of the power sector will increase to 5102.9 Mt by 2025. Consequently, policy recommendations are proposed to achieve low-carbon development in aspects of population, technology, and economy.

scholarly journals Driving Factor Analysis of Carbon Emissions in China’s Power Sector for Low-Carbon Economy

2017 ◽  
Vol 2017 ◽  
pp. 1-10 ◽  
Author(s):  
Dan Yan ◽  
Yalin Lei ◽  
Li Li
Keyword(s):  
Power Generation ◽  
Carbon Emissions ◽  
Electricity Consumption ◽  
Least Squares Regression ◽  
Low Carbon ◽  
Power Sector ◽  
Stirpat Model ◽  
Coal Consumption ◽  
Low Carbon Economy ◽  
Generation Structure

The largest percentage of China’s total coal consumption is used for coal-fired power generation, which has resulted in the power sector becoming China’s largest carbon emissions emitter. Most of the previous studies concerning the driving factors of carbon emissions changes lacked considerations of different socioeconomic factors. This study examines the impacts of eight factors from different aspects on carbon emissions within power sector from 1981 to 2013 by using the extended Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model; in addition, the regression coefficients are effectively determined by a partial least squares regression (PLS) method. The empirical results show that (1) the degree of influence of various factors from strong to weak is urbanization level (UL) > technology level (T1) > population (P) > GDP per capita (A) > line loss (T2) > power generation structure (T3) > energy intensity (T4) > industry structure (IS); (2) economic activity is no longer the most important contributing factor; the strong correlation between electricity consumption and economic growth is weakening; and (3) the coal consumption rate of power generation had the most obvious inhibitory effect, indicating that technological progress is still a vital means of achieving emissions reductions.

Testing of Fiber Contents in Mixture Textiles by NIR Combined with BP Neural Network

2014 ◽  
Vol 651-653 ◽  
pp. 301-304
Author(s):  
Li Liu ◽  
Li Yan ◽  
Yao Cheng Xie
Keyword(s):  
Neural Network ◽  
Bp Neural Network ◽  
Near Infrared ◽  
Human Life ◽  
Back Propagation ◽  
Fiber Content ◽  
Partial Least Square ◽  
Least Square ◽  
Neural Network Models ◽  
Mixture Samples

Textiles are necessaries of human life. The fiber content is index of textile quality and how to measure it has important meaning. A method for testing fiber contents in mixture textiles by near infrared spectroscopy (NIR) was researched. The near infrared Spectra of samples in the range of 4000 cm-1 - 10000 cm-1 were obtained. Noise reduction and compression of spectra data was done by wavelet transform (WT). The reconstructed spectral signals were established based on WT and the correction models based on back propagation (BP) neural network were built. Comparisons between the BP neural network models at different analysis scale and the model of partial least square method (PLS) were given. When the structure of neural network is 11-9-2 for cotton/ terylene mixture samples and 21-13-2 for cotton/wool mixture samples, the best accuracy and fastest convergence speed is achieved. Experimental results have shown that this approach by Fourier transform NIR based on the BP neural network to predict the fiber content of textile mixture can satisfy the requirement of quantitative analysis and is also suitable for other fiber contents measurement of mixture textiles.

scholarly journals A Smart Helmet-Based PLS-BPNN Error Compensation Model for Infrared Body Temperature Measurement of Construction Workers during COVID-19

Mathematics ◽  
2021 ◽  
Vol 9 (21) ◽  
pp. 2808
Author(s):  
Li Li ◽  
Jiahui Yu ◽  
Hang Cheng ◽  
Miaojuan Peng
Keyword(s):  
Neural Network ◽  
Body Temperature ◽  
Temperature Measurement ◽  
Error Compensation ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Partial Least Square ◽  
Least Square ◽  
Least Square Regression ◽  
Bpnn Model

In the context of the long-term coexistence between COVID-19 and human society, the implementation of personnel health monitoring in construction sites has become one of the urgent needs of current construction management. The installation of infrared temperature sensors on the helmets required to be worn by construction personnel to track and monitor their body temperature has become a relatively inexpensive and reliable means of epidemic prevention and control, but the accuracy of measuring body temperature has always been a problem. This study developed a smart helmet equipped with an infrared temperature sensor and conducted a simulated construction experiment to collect data of temperature and its influencing factors in indoor and outdoor construction operation environments. Then, a Partial Least Square–Back Propagation Neural Network (PLS-BPNN) temperature error compensation model was established to correct the temperature measurement results of the smart helmet. The temperature compensation effects of different models were also compared, including PLS-BPNN with Least Square Regression (LSR), Partial Least Square Regression (PLSR), and single Back Propagation Neural Network (BPNN) models. The results showed that the PLS-BPNN model had higher accuracy and reliability, and the determination coefficient of the model was 0.99377. After using PLS-BPNN model for compensation, the relative average error of infrared body temperature was reduced by 2.745 °C and RMSE was reduced by 0.9849. The relative error range of infrared body temperature detection was only 0.005~0.143 °C.

scholarly journals Analysis of CO2 Drivers and Emissions Forecast in a Typical Industry-Oriented County: Changxing County, China

Energies ◽  
2020 ◽  
Vol 13 (5) ◽  
pp. 1212 ◽  
Author(s):  
Yao Qian ◽  
Lang Sun ◽  
Quanyi Qiu ◽  
Lina Tang ◽  
Xiaoqi Shang ◽  
...  
Keyword(s):  
Co2 Emissions ◽  
Industrial Structure ◽  
Ghg Emissions ◽  
Energy Structure ◽  
Low Carbon ◽  
Stirpat Model ◽  
Carbon Reduction ◽  
Economic Output ◽  
Low Carbon Development ◽  
And Control

Decomposing main drivers of CO2 emissions and predicting the trend of it are the key to promoting low-carbon development for coping with climate change based on controlling GHG emissions. Here, we decomposed six drivers of CO2 emissions in Changxing County using the Logarithmic Mean Divisia Index (LMDI) method. We then constructed a model for CO2 emissions prediction based on a revised version of the Stochastic Impacts by Regression on Population, Affluence, and Technology (STIRPAT) model and used it to simulate energy-related CO2 emissions in five scenarios. Results show that: (1) From 2010 to 2017, the economic output effect was a significant, direct, dominant, and long-term driver of increasing CO2 emissions; (2) The STIRPAT model predicted that energy structure will be the decisive factor restricting total CO2 emissions from 2018 to 2035; (3) Low-carbon development in the electric power sector is the best strategy for Changxing to achieve low-carbon development. Under the tested scenarios, Changxing will likely reach peak total CO2 emissions (17.95 million tons) by 2030. Measures focused on optimizing the overall industrial structure, adjusting the internal industry sector, and optimizing the energy structure can help industry-oriented counties achieve targeted carbon reduction and control, while simultaneously achieving rapid economic development.

scholarly journals MODEL OUTPUT STATISTICS DENGAN PRINCIPAL COMPONENT REGRESSION, PARTIAL LEAST SQUARE REGRESSION, DAN RIDGE REGRESSION UNTUK KALIBRASI PRAKIRAAN CUACA JANGKA PENDEK

2021 ◽  
Vol 10 (3) ◽  
pp. 355
Author(s):  
NISWATUL QONA’AH ◽  
HASIH PRATIWI ◽  
YULIANA SUSANTI
Keyword(s):  
Ridge Regression ◽  
Principal Component Regression ◽  
Principal Component ◽  
Partial Least Square ◽  
Least Square ◽  
Partial Least Square Regression ◽  
Model Output ◽  
Mean Square ◽  
Least Square Regression ◽  
Model Output Statistics

Penelitian ini merupakan upaya pengembangan Model Output Statistics (MOS) yang akan digunakan sebagai alat kalibrasi prakiraan cuaca jangka pendek. Informasi mengenai prakiraan cuaca yang akurat diharapkan dapat meminimalkan risiko kecelakaan yang disebabkan oleh cuaca, khususnya dalam bidang transportasi udara dan laut. Metode yang akan dikembangkan mencakup beberapa stasiun pengamatan cuaca di Indonesia. MOS merupakan sebuah metode berbasis regresi yang mengoptimalkan hubungan antara observasi cuaca dan luaran model Numerical Weather Predictor (NWP). Beberapa masalah yang muncul kaitannya dengan MOS adalah; mereduksi dimensi luaran NWP, mendapatkan variabel prediktor yang mampu menjelaskan variabilitas variabel respon, dan menentukan metode statistik yang sesuai dengan karakteristik data, sehingga dapat menggambarkan hubungan antara variabel respon dan variabel prediktor. Tujuan dari penelitian ini yaitu untuk mendapatkan pemodelan MOS yang sesuai untuk variabel respon suhu maksimum, suhu minimum, dan kelembapan udara. Metode regresi yang digunakan adalah Principal Component Regression (PCR), Partial Least Square Regression (PLSR), dan ridge regression. Selanjutnya, model MOS yang terbentuk divalidasi dengan kriteria Root Mean Square Error (RMSE) dan Percentage Improval (IM%). MOS mampu mengoreksi bias prakiraan NWP hingga lebih dari 50%. Berdasarkan RMSE terkecil pada penelitian ini, suhu maksimum lebih akurat diprakirakan menggunakan model PLSR, sementara suhu minimum dan kelembapan udara lebih akurat diprakirakan menggunakan ridge regression.Kata Kunci: cuaca, MOS, NWP.

scholarly journals Effect of Storage Time and Packing Method on the Freshness of Dried Lycium Fruit Using Electronic Nose and Chemometrics

2020 ◽  
Vol 2020 ◽  
pp. 1-8
Author(s):  
Xiaojing Tian ◽  
Ming Long ◽  
Yuanlin Liu ◽  
Peng Zhang ◽  
Xiaoqin Bai ◽  
...  
Keyword(s):  
Storage Time ◽  
Sensor Array ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Partial Least Square ◽  
Least Square ◽  
Pls Analysis ◽  
And Cluster Analysis ◽  
Metal Oxide Sensor ◽  
Packing Method

The effect of storage time and packing method on dried Lycium fruits was studied through an electronic olfactory system with the metal oxide sensor array that provides an overall perception of the volatile compounds presented in the sample headspace. Principle component analysis (PCA), canonical discriminant analysis (CDA), and cluster analysis (CA) were used for freshness and packing methods discrimination of dried Lycium fruits. The stale samples of 2015 and 2016 could be separated with those of 2017 by PCA, CDA, and CA analysis. Better discrimination results were obtained by CDA, with samples of 2015 and 2016 separated with each other. For samples of 2017, the unpackaged samples of 2017-4 were distinguished with the vacuumed samples, while samples of grade C were separated with B and D. For quantitative analysis, predictive models for prediction of the storage years of dried Lycium fruits were built with methods of partial least square (PLS) analysis, multiple linear regression (MLR), and back propagation neural network (BPNN). The model built by BPNN showed the best predict ability with R2 = 0.9994, while PLS and MLR were also effective in the prediction of storage years of dried Lycium fruits, with high determination coefficients of 0.9316 and 0.9330. These findings showed that E-nose can be used in the discrimination of the storage time and package method of dried Lycium fruits.

scholarly journals An Improved Generalized Predictive Control in a Robust Dynamic Partial Least Square Framework

2015 ◽  
Vol 2015 ◽  
pp. 1-14
Author(s):  
Jin Xin ◽  
Chi Qinghua ◽  
Liu Kangling ◽  
Liang Jun
Keyword(s):  
Markov Model ◽  
Predictive Control ◽  
Detection Method ◽  
Hidden Markov ◽  
Radial Basis Function Network ◽  
Partial Least Square ◽  
Least Square ◽  
Generalized Predictive Control ◽  
Model Mismatch ◽  
Function Network

To tackle the sensitivity to outliers in system identification, a new robust dynamic partial least squares (PLS) model based on an outliers detection method is proposed in this paper. An improved radial basis function network (RBFN) is adopted to construct the predictive model from inputs and outputs dataset, and a hidden Markov model (HMM) is applied to detect the outliers. After outliers are removed away, a more robust dynamic PLS model is obtained. In addition, an improved generalized predictive control (GPC) with the tuning weights under dynamic PLS framework is proposed to deal with the interaction which is caused by the model mismatch. The results of two simulations demonstrate the effectiveness of proposed method.

MOTIVASI MEMEDIASI PENGARUH PELATIHAN DAN LINGKUNGAN KERJA TERHADAP KINERJA KARYAWAN PT. TELKOM WITEL YOGYAKARTA

2020 ◽  
Vol 28 (1) ◽  
pp. 71-88
Author(s):  
Tyas Tunjung Sari ◽  
Pandu Nuansa Luhur
Keyword(s):  
Work Environment ◽  
Significant Influence ◽  
Structural Equation ◽  
Employee Performance ◽  
Partial Least Square ◽  
Least Square ◽  
Primary Data ◽  
Version 2.0 ◽  
Analyze Data

This study aims to determine the motivation of work to mediate the effect of training and work environment on employee performance at PT. Telkom Witel Yogyakarta Yogyakarta. The purpose of this study is to determine and analyze 1) the effect of training on employee performance at PT. Telkom Witel Yogyakarta 2) the effect of training on employee performance through motivation at PT. Telkom Witel Yogyakarta 3) the influence of the work environment on employee performance at PT. Telkom Witel Yogyakarta 4) the influence of the work environment on employee performance through motivation at PT. Telkom Witel Yogyakarta. This study uses primary data through research on 62 respondents. Structural Equation is used to analyze data, using PLS (Partial Least Square) version 2.0. The results of this study indicate that there are 1) positive and significant influence of training on employee performance 2) positive and significant influence of work environment on employee performance 3) positive and significant effect of training on employee performance through motivation 4) positive and significant influence of work environment on employee performance through motivation.

Locus Of Control Sebagai Varibel Moderasi Hubungan Kompleksitas Tugas Dan Kinerja Auiditor

2018 ◽  
Vol 16 (2) ◽  
pp. 113
Author(s):  
Sri Hastuti ◽  
Siti Sundari
Keyword(s):  
Locus Of Control ◽  
Big Five ◽  
Partial Least Square ◽  
Least Square

Research Objectives to prove the influence of the complexity of the tasks faced by the Auditor on performance in carrying out duties as an Auditor. The complexity of tasks related to various problems in the company requires locus of control from internal and external to maintain independence and competence.The first auditor performance case occurred in 2002 with the disclosure of the Enron case involving the KAP in the big five, Athur Anderson. In 2008 the Telkom case affected the closure of KAP Edy Priyanto, and there were still many other cases which were violations of the accountant's code of ethics.This research is in the form of quantitative, with proof of the complexity of the task and locus of control on the performance of the auditor. Sample 46 Junior auditors from several KAPs in Surabaya, using the Partial Least Square test, the result that the complexity of the task affects the performance of the Auditor and the interaction of the complexity of the task with locus of control does not affect the performance of the Auditor.

ANALYSIS THE IMPACT OF THE IMPLEMENTATION OF QUALITY MANAGEMENT SYSTEM (QMS) AND SAFETY AND HEALTHY AT WORK MANAGEMENT SYSTEM (SHWMS / SMK3) ON THE JOB PERFORMANCE OF EMPLOYEES

2018 ◽  
Vol 3 (01) ◽  
pp. 45
Author(s):  
Nur Hidayat ◽  
Indah Kusuma Hayati
Keyword(s):  
Quality Management ◽  
Job Performance ◽  
Latent Variables ◽  
Management System ◽  
Quality Management System ◽  
Partial Least Square ◽  
Least Square ◽  
Global Challenges ◽  
The Impact ◽  
Work Management

Recently, the evolvement of globalization era has been the global challenges that cannot be avoided either by private or government sectors, and they are requested to be survived encountering such the condition. The implementation of Quality Management System (QMS) in the operational company is the way how to guarantee the quality of products or services offered to the people. One of the purposes of QMS implementation is to provide a prime satisfaction to the customers. The impact of QMS implementation is expected to increase job performance of the employees. Besides the implementation of Quality Management System (QMS), the impact of global challenges has been increasing the competitive efforts to execute more effective production process. However, it has required manpower protection accordingly. This research aims to find out whether the implementation of quality management system and safety and healthy at work management system have impacted on the job performance of employees. Objects of this research are the employees in the production department at PT Guna Senaputra Sejahtera Plant 1 Bogor. Data analysis technique of this research has applied software Smart PLS (Partial Least Square). PLS has estimated a model of correlation among the latent variables and correlation between latent variables and its indicators. Result of data processing has indicated that the implementation of Quality Management System (QMS) and system of safety and healthy at work have positively and significantly impacted job performance of employees.Keywords : Quality Management System (QMS), Safety and Healthy at Work System ( SHWS / SMK3), and Job Performance of Employees

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