Can China Meet Its 2030 Total Energy Consumption Target? Based on an RF-SSA-SVR-KDE Model

Cui, Xiwen; Guan, Xinyu; Wang, Dongyu; Niu, Dongxiao; Xu, Xiaomin

doi:10.3390/en15166019

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Support vector regression (SVR) and least squares support vector regression (LSSVR) have also been employed by researchers to predict intervals [31][32][33]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

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A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction

Feng

Yao

et al. 2023

Energies

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Although the machine-learning model demonstrates high accuracy in wind speed prediction, it struggles to accurately depict the fluctuation range of the predicted values due to the inherent uncertainty in wind speed sequences. To address this limitation and enhance the reliability, we propose an effective wind speed interval prediction model that combines twin support vector regression (TSVR), variational mode decomposition (VMD), and the slime mould algorithm (SMA). In our methodology, the complex wind speed series is decomposed into multiple relatively stable subsequences using the VMD method. The principal component and residual series are then subject to interval prediction using the TSVR model, while the remaining components undergo point prediction. The SMA method is employed to search for optimal parameter combinations. The prediction interval of wind speed is obtained by aggregating the forecasting results of all TSVR models for each subseries. Our proposed model has demonstrated superior performance in various applications. It ensures that the wind speed value falls within the designated interval range while achieving the narrowest prediction interval. For instance, in the spring dataset with 1-period, we obtained a predicted interval with a prediction intervals coverage probability (PICP) value of 0.9791 and prediction interval normalized range width (PINRW) value of 0.0641. This outperforms other comparative models and significantly enhances its practical application value. After adding the residual interval prediction model, the reliability of the prediction interval is significantly improved. As a result, this study presents a novel twin support vector regression model as a valuable approach for multi-step wind speed interval prediction.

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“…Support vector regression (SVR) and least squares support vector regression (LSSVR) have also been employed by researchers to predict intervals [31][32][33]. In Ref.…”

Section: Introductionmentioning

confidence: 99%

“…In Ref. [31], a random forest model is initially used to screen for influential factors for the model, while the sparrow search algorithm and SVR model are then utilized to forecast China's future total energy consumption. To enhance the predictive relevance of the model, interval forecasting is implemented using the KDE approach.…”

Section: Introductionmentioning

confidence: 99%

A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction

Feng

Yao

et al. 2023

Energies

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Prediction and evaluation of the energy structure under the green finance development in Chongqing municipality, China

Zeng,

Yu,

2023

Heliyon

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Environmental Hazards and Risk Identification in the Arctic Shelf Development as Part of China and Russia Energy Interests

Razmanova

Pisarenko²,

Нестерова

et al. 2023

Energies

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China and Russia have different interests in the Arctic but are forced to look for possible ways of cooperation in energy projects in the current external conditions. This changes the priorities of both countries and, accordingly, transforms the risks. Objectives of the research: to build an algorithm for identifying anthropogenic environmental risks in the context of two major players economic activities in the Arctic region: the Russian Federation and China. In the paper, we formulated an algorithm of environmental risk identification. We identified environmental hazards from the main parameter—the type of economic activity for the extraction of energy resources, premises, and factors for the occurrence of environmental hazards and compiled criteria for risk selection. Methods used: complex analysis (mixed method research): empirical and comparative methods, methods of expert assessments, the method of inductive statistics (inferential statistics) to compare the perception of risk at the level of different groups. Results: the algorithm has been formed for determining risks in the changed external conditions. Major anthropogenic environmental risks are identified from the perspective of the main players in the Arctic—Russia and China, which can help to make the necessary decisions on time and partially prevent environmental degradation. This makes it possible to identify the risks associated with conducting economic activities for the extraction of energy resources in the Arctic region. Therefore, this study contributes to a more accurate identification of anthropogenic environmental risks in the Arctic region.

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Can China Meet Its 2030 Total Energy Consumption Target? Based on an RF-SSA-SVR-KDE Model

Cited by 3 publications

References 31 publications

A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction

A Novel Twin Support Vector Regression Model for Wind Speed Time-Series Interval Prediction

Prediction and evaluation of the energy structure under the green finance development in Chongqing municipality, China

Environmental Hazards and Risk Identification in the Arctic Shelf Development as Part of China and Russia Energy Interests

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