2022
DOI: 10.3389/fenrg.2021.783786
|View full text |Cite
|
Sign up to set email alerts
|

Regulation Effect of Smart Grid on Green Transformation of Electric Power Enterprises: Based on the Investigation of “Leader” Trap

Abstract: The 2060 carbon neutral target reflects the long-term equilibrium and stability of production activities and the natural environment. As an important part of Chinese energy structure, the operation and transformation of power enterprises will face higher requirements. Although the rapid development of smart grids provides necessary technical support for power enterprises to build a modern energy system with green power as the core, whether power enterprises can use smart grids to improve their operating perfor… Show more

Help me understand this report

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

0
4
0

Year Published

2022
2022
2024
2024

Publication Types

Select...
5

Relationship

0
5

Authors

Journals

citations
Cited by 6 publications
(5 citation statements)
references
References 37 publications
0
4
0
Order By: Relevance
“…In the future, the smart grid systems can be fully operated by robotic technology as faults in the system can be cleared within a short span of time with equal regularization. Machine learning algorithm Taguchi loss function 65 [8] Heuristic algorithm Convex optimization 67 [17] Long short-term memory (LSTM) Multidirectional cyber physical systems 68 [18] Autoregressive indicated moving average (ARIMA) Online information networks 72 [19] Adaptive ARIMA Nonlinear weighted inputs [20][21][22][23][24][25][26] Linear regression Extreme learning machine 74 Proposed Support vector machines (SVM) Regularization parameters 81 10 Wireless Communications and Mobile Computing…”
Section: Discussionmentioning
confidence: 99%
See 1 more Smart Citation
“…In the future, the smart grid systems can be fully operated by robotic technology as faults in the system can be cleared within a short span of time with equal regularization. Machine learning algorithm Taguchi loss function 65 [8] Heuristic algorithm Convex optimization 67 [17] Long short-term memory (LSTM) Multidirectional cyber physical systems 68 [18] Autoregressive indicated moving average (ARIMA) Online information networks 72 [19] Adaptive ARIMA Nonlinear weighted inputs [20][21][22][23][24][25][26] Linear regression Extreme learning machine 74 Proposed Support vector machines (SVM) Regularization parameters 81 10 Wireless Communications and Mobile Computing…”
Section: Discussionmentioning
confidence: 99%
“…Performance Measurements. The effectiveness of the integrated algorithm can be proved by simulating the perfor-mance of the proposed method and comparing it with existing models using the terms such as mean absolute percentage error (MAPE), mean absolute error (MAE), and mean square error (MSE) [21][22][23][24][25][26]. From Figure 6 and Table 3, it is perceived that total number of iterations are varied from 10 to 100, and for each iteration periods, the percentage errors are measured.…”
Section: Scenariomentioning
confidence: 99%
“…It analyzes text, audio and images to learn people's opinions, attitudes and emotions [ 62 ]. The sentimental satisfaction degree of the public with government management is an important issue concerned by academic and political circles [ 63 ]. It provides a reference for the further implementation of government work.…”
Section: Literature Reviewmentioning
confidence: 99%
“…With the development of digital revolution, the industrial structures have been continuously optimized and upgraded [1][2], the forms and functions of the power grid has gradually changed [3][4][5], which make many new forms of power consumption have emerged [6][7]. As the high-tech manufacturing enterprises grows [8][9], a large number of loads which are sensitive to voltage sags have been connected to the power grid [10], bringing new opportunities and challenges to develop premium power.…”
Section: Introductionmentioning
confidence: 99%