Drivers analysis and empirical mode decomposition based forecasting of energy consumption structure

Xia, Chenxia; Wang, Zilong

doi:10.1016/j.jclepro.2020.120107

Cited by 36 publications

(12 citation statements)

References 47 publications

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“…Factor analysis was chosen as an additional methodological analysis tool, which allows us to evaluate the contribution of individual components of companies to economic efficiency. For this, we used the decomposition of the ROE indicator into five key factors, the so-called Dupont model decomposition and the LMDI-1 method (Logarithmic Mean Divisia Index) for factor analysis [5,6].…”

Section: Methodsmentioning

confidence: 99%

Factor analysis of economic efficiency of the oil and gas industry in Russia

2020

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The article presents an analysis of performance indicators of companies in the oil and gas industry of Russia in 2013-2018. The aim of the study is to decompose the factors of change in the return on equity (ROE) of Russian oil and gas companies. The authors used the Dupont method to identify the main factors affecting ROE. After decomposing the ROE into factors, the authors evaluated their impact using the Logarithmic Mean Divisia Index (LMDI-1). As a result of the study, it was found that in most companies the greatest impact on the change in ROE has a return on sales, primarily as a result of revenue growth due to favorable oil prices and positive exchange rate differences. Simultaneously, sales profitability has a positive effect on the index of almost all companies in the industry. Asset turnover also had only a positive effect on ROE. However, a comparison of the results for 2017 and 2018 showed a significant difference in performance for many companies. Thus, we can conclude that the effect of a certain factor on the profitability of sales largely depends on the development strategy of the company for the coming year, the economic and political situation.

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Section: Methodsmentioning

confidence: 99%

Factor analysis of economic efficiency of the oil and gas industry in Russia

2020

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“…China's energy demand will be met by the use of coal for a long time to come. [1][2][3][4] With the increase of coal mining intensity and the rapid growth of mining depth, the problems of coal and gas outburst are becoming more serious. Therefore, in coal mining, effective technical measures must be taken to reduce the outburst.…”

Section: Introductionmentioning

confidence: 99%

Variation law of roof stress and permeability enhancement effect of repeated hydraulic fracturing in low‐permeability coal seam

Liangwei

2021

Energy Science & Engineering

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“…The steam coal price indicates the general performance of supply and demand on the steam coal market. China is a big consumer of coal (Xiong and Xu, 2021;Wang and Du, 2020). Forecasting the steam coal price accurately can help in the analysis of the steam coal market, grasp the implied law in the steam coal market, and improve the steam coal market's efficiency.…”

Section: Introductionmentioning

confidence: 99%

“…The combined model can improve the model performance based on the advantages of the sub-model (Wang et al, 1210;Zhou et al, 2019;Wang et al, 2020a;Wang et al, 2020b;Qiao et al, 2021;Zhang et al, 2021). This method can decompose the forecasting error of the forecasting model into multiple modal components by using the EMD method (Yu et al, 2008;Xu et al, 2019;Xia and Wang, 2020), build the ARIMA model (Conejo et al, 2005;Karabiber and Xydis, 2019) for each modal component for forecasting, and add up the forecasted values of all the modal components to compensate error for the original forecasting model.…”

Section: Introductionmentioning

confidence: 99%

Forecasting of Steam Coal Price Based on Robust Regularized Kernel Regression and Empirical Mode Decomposition

Tang

et al. 2021

Front. Energy Res.

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Aiming at the problem of difficulties in modeling the nonlinear relation in the steam coal dataset, this article proposes a forecasting method for the price of steam coal based on robust regularized kernel regression and empirical mode decomposition. By selecting the polynomial kernel function, the robust loss function and L2 regular term to construct a robust regularized kernel regression model are used. The polynomial kernel function does not depend on the kernel parameters and can mine the global rules in the dataset so that improves the forecasting stability of the kernel model. This method maps the features to the high-dimensional space by using the polynomial kernel function to transform the nonlinear law in the original feature space into linear law in the high-dimensional space and helps learn the linear law in the high-dimensional feature space by using the linear model. The Huber loss function is selected to reduce the influence of abnormal noise in the dataset on the model performance, and the L2 regular term is used to reduce the risk of model overfitting. We use the combined model based on empirical mode decomposition (EMD) and auto regressive integrated moving average (ARIMA) model to compensate for the error of robust regularized kernel regression model, thus making up for the limitations of the single forecasting model. Finally, we use the steam coal dataset to verify the proposed model and such model has an optimal evaluation index value compared to other contrast models after the model performance is evaluated as per the evaluation index such as RMSE, MAE, and mean absolute percentage error.

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Drivers analysis and empirical mode decomposition based forecasting of energy consumption structure

Cited by 36 publications

References 47 publications

Factor analysis of economic efficiency of the oil and gas industry in Russia

Factor analysis of economic efficiency of the oil and gas industry in Russia

Variation law of roof stress and permeability enhancement effect of repeated hydraulic fracturing in low‐permeability coal seam

Forecasting of Steam Coal Price Based on Robust Regularized Kernel Regression and Empirical Mode Decomposition

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