Fisher information and online SVR-based dynamic modeling methodology for meteorological sensitive load forecasting in smart grids

Cai, Shuping; Sun, Zhong‐Ming; Yan, Juntao; Tang, Dahang; Chen, Yan; Zhou, Zongqing

doi:10.1007/s00202-021-01308-3

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…In this paper, the three-dimensional cloud characterized by particles is regarded as a volume, and Splatting algorithm is used to draw it. The incident light intensity of particles is calculated offline and the outgoing light intensity of particles is calculated in real time [7] .…”

Section: Cloud Particle Modeling and Renderingmentioning

confidence: 99%

Meteorological data modeling and 3D visualization based on adaptive grid structure

Lin

Huang

Zhong

2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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Aiming at the problems of high modeling complexity and low rendering efficiency of existing visualization methods of real meteorological cloud data, a 3D visualization method of meteorological cloud data based on adaptive far-field grid structure of region of interest is proposed. Methods The region of interest was extracted to generate an adaptive far-field grid structure, which was applied to cloud particle modeling. The fine resolution of the region of interest was kept, and the number of particles in other regions was optimized. Finally, the rendering of 3D cloud images was completed. Simulation results based on WRF model meteorological cloud data show that the above grid structure can speed up rendering and rendering on the basis of ensuring the rendering quality, and can better display the morphology and structural characteristics of real clouds.

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Section: Cloud Particle Modeling and Renderingmentioning

confidence: 99%

Meteorological data modeling and 3D visualization based on adaptive grid structure

Lin

Huang

Zhong

2023

Second Guangdong-Hong Kong-Macao Greater Bay Area Artificial Intelligence and Big Data Forum (AIBDF 2022)

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“…The power load itself is a random non-stationary sequence, and the statistical modeling method generally does not consider meteorological information, holiday information and other influencing factors, which leads to the failure to reflect the nonlinear characteristics that affect the load fluctuation. SVR(Support Vector Regression) method combines meteorological factors to realize dynamic modeling on SVM 5 . Literature [6][7] has constructed several different forecasting models, mainly including BPNN, GRNN (General Regression Neural Network) and so on, which can consider various external factors at the same time.…”

Section: Introductionmentioning

confidence: 99%

Research on short-term power load forecasting and analysis based on EEMD-LSSVM- integrated BPNN

Liu,

Xie,

Tang

2023

Seventh International Conference on Mechatronics and Intelligent Robotics (ICMIR 2023)

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Machine learning-based very short-term load forecasting in microgrid environment: evaluating the impact of high penetration of PV systems

et al. 2022

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Fisher information and online SVR-based dynamic modeling methodology for meteorological sensitive load forecasting in smart grids

Cited by 5 publications

References 25 publications

Meteorological data modeling and 3D visualization based on adaptive grid structure

Meteorological data modeling and 3D visualization based on adaptive grid structure

Research on short-term power load forecasting and analysis based on EEMD-LSSVM- integrated BPNN

Machine learning-based very short-term load forecasting in microgrid environment: evaluating the impact of high penetration of PV systems

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