Harnessing the Power of Neural Networks for Predicting Shading

Mahto, Rakeshkumar; Sood, Kanika

doi:10.1109/ghtc56179.2023.10354791

Cited by 2 publications

(1 citation statement)

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“…This approach aims to enhance the overall complexity of feature selection, the previously proposed hybrid model is refined to further improve the correlation selection and processing speed of features. In [9], a variational mode decomposition (VMD) and empirical mode decomposition (EMD) method is proposed. The amplitude-frequency characteristics of the photovoltaic output power sequence are extracted by VMD, and the residual term is decomposed by the second EMD to extract more features.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Generation Related Physical Quantity Mining Method Based On Historical Time Series Data Analysis

Wang,

Liao,

Shen

2024

J. Phys.: Conf. Ser.

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The impact of distributed photovoltaic grid-connected on distribution network security, power quality and system stability cannot be ignored. In order to better cope with the uncertainty and output of new energy output and understand the characteristics of distributed new energy power output, it is necessary to predict photovoltaic power. The historical time series data of photovoltaic power is large in dimension and quantity. If the data output is not carried out, a large amount of redundant data will affect the accuracy of photovoltaic prediction. Therefore, this paper proposes a feature selection method based on MIC most mutual information coefficient, which filters out the most relevant data of photovoltaic power generation from the original feature variables, and then performs the feature dimension reduction method of linear discriminant analysis (LDA) to map the high-dimensional data to a lower dimension space. Finally, using the prediction simulation example, using the long and short time neural network (LSTM) prediction comparison, the feature dimension reduction method of MIC feature selection and LDA effectively improves the accuracy of photovoltaic power generation prediction.

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Section: Introductionmentioning

confidence: 99%