Deep neural network for forecasting of photovoltaic power based on wavelet packet decomposition with similar day analysis

Liu, Xiangjie; Liu, Yuanyan; Kong, Xiaobing; Besheer, Ahmad H.; Lee, Kwang Y.

doi:10.1016/j.energy.2023.126963

Cited by 16 publications

(2 citation statements)

References 32 publications

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“…The LightGBM algorithm offers flexibility to cater to various requirements such as model complexity control, computing efficiency, memory control, and different fields [28]. To optimize the LightGBM algorithm and enhance recognition accuracy, this paper utilizes the AVOA's strong global search ability to optimize four parameters.…”

Section: Avoa-lightgbm Power Fiber Optic Cable Event Pattern Recognit...mentioning

confidence: 99%

AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition

Chen,

Cui,

Zhang

2023

Electronics

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The type of power fiber optic cable fault event obtained by analyzing the optical time domain reflectometer (OTDR) detection curve is an important basis for ensuring the operation quality of communication lines. To address the issue of low accuracy in recognizing fault event patterns, this research proposes the AVOA-LightGBM method for optical cable fault event pattern recognition based on wavelet packet decomposition. Initially, a three-layer wavelet packet decomposition is performed on different fault events, resulting in eight characteristic signals. These signals are then normalized and used as input for each recognition model. The Light Gradient Boosting Machine (LightGBM) is optimized using the African vulture optimization algorithm (AVOA) for pattern recognition. The experimental results demonstrate that this method achieves a recognition accuracy of 98.24%. It outperforms LightGBM, support vector machine (SVM), and extreme learning machine (ELM) by 3.7%, 19.15%, and 5.67%, respectively, in terms of accuracy. Moreover, it shows a 1.8% improvement compared with the combined model PSO-LightGBM.

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Section: Avoa-lightgbm Power Fiber Optic Cable Event Pattern Recognit...mentioning

confidence: 99%

AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition

Chen,

Cui,

Zhang

2023

Electronics

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“…A quantile regression-convolutional neural network-bidirectional long short-term memory (QR-CNN-BiLSTM) combined prediction model was proposed to realize the short-term interval prediction of photovoltaic power. In [18], Liu, X. stated that the levy-flight beetle antennae search (LFBAS) algorithm is used to search similar days, and the historical days similar to the forecast day are selected from the historical data in real time. Finally, the searched data of similar days are decomposed, reconstructed, and input into the GRUs to establish the prediction model of photovoltaic power generation.…”

Section: Introductionmentioning

confidence: 99%

Photovoltaic Power Prediction Based on Irradiation Interval Distribution and Transformer-LSTM

Liao,

Min,

et al. 2024

Energies

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Accurate photovoltaic power prediction is of great significance to the stable operation of the electric power system with renewable energy as the main body. In view of the different influence mechanisms of meteorological factors on photovoltaic power generation in different irradiation intervals and that the data-driven algorithm has the problem of regression to the mean, in this article, a prediction method based on irradiation interval distribution and Transformer-long short-term memory (IID-Transformer-LSTM) is proposed. Firstly, the irradiation interval distribution is calculated based on the boxplot. Secondly, the distributed data of each irradiation interval is input into the Transformer-LSTM model for training. The self-attention mechanism of the Transformer is applied in the coding layer to focus more important information, and LSTM is applied in the decoding layer to further capture the potential change relationship of photovoltaic power generation data. Finally, sunny data, cloudy data, and rainy data are selected as test sets for case analysis. Through experimental verification, the method proposed in this article has a certain improvement in prediction accuracy compared with the traditional methods under different weather conditions. In the case of local extrema and large local fluctuations, the prediction accuracy is clearly improved.

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A novel hybrid intelligent approach for solar photovoltaic power prediction considering UV index and cloud cover

Aman,

Rizwan,

Kumar

2024

Electr Eng

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Deep neural network for forecasting of photovoltaic power based on wavelet packet decomposition with similar day analysis

Cited by 16 publications

References 32 publications

AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition

AVOA-LightGBM Power Fiber Optic Cable Event Pattern Recognition Method Based on Wavelet Packet Decomposition

Photovoltaic Power Prediction Based on Irradiation Interval Distribution and Transformer-LSTM

A novel hybrid intelligent approach for solar photovoltaic power prediction considering UV index and cloud cover

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