Ultra-short-term Photovoltaic Power Prediction Based on VMD-LSTM-RVM Model

Wang, Su; Wei, Leifeng; Zeng, Liang

doi:10.1088/1755-1315/781/4/042020

Cited by 15 publications

(12 citation statements)

References 8 publications

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“…Further to improve the prediction accuracy, Zhou et al [45] proposed the Attention-LSTM model to predict the ultra-short-term photovoltaic power. Wang et al [46] used modal decomposition to decompose the time series and then the short-term power generation of the photovoltaic system is predicted using LSTM and the decomposed time series. Meng et al [47] used multiple deep-learning models to predict multiple outcomes.…”

Section: Photovoltaic Power Prediction Methodsmentioning

confidence: 99%

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Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction

Guo

Lai

Zhang

et al. 2023

IET Computer Vision

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Clean energy is a major trend. The importance of photovoltaic power generation is also growing. Photovoltaic power generation is mainly affected by the weather. It is full of uncertainties. Previous work has relied chiefly on historical photovoltaics data for time series forecasts. However, unforeseen weather conditions can sometimes skew. Consequently, a spatial‐temporal‐meteorological‐long short‐term memory prediction model (STM‐LSTM) is proposed to compensate for the shortage of photovoltaic prediction models for uncertainties. This model can simultaneously process satellite image data, historical meteorological data, and historical power generation data. In this way, historical patterns and meteorological change information are extracted to improve the accuracy of photovoltaic prediction. STM‐LSTM processes raw satellite data to obtain cloud image data. It can extract cloud motion information using the dense optical flow method. First, the cloud images are processed to extract cloud position information. By adaptive attentive learning of images in different bands, a better representation for subsequent tasks can be obtained. Second, it is important to process historical meteorological data to learn meteorological change patterns. Last but not least, the historical photovoltaic power generation sequences are combined to obtain the final photovoltaic prediction results. After a series of experimental validation, the performance of the proposed STM‐LSTM model has a good improvement compared with the baseline model.

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Section: Photovoltaic Power Prediction Methodsmentioning

confidence: 99%

“…Wang et al. [46] used modal decomposition to decompose the time series and then the short‐term power generation of the photovoltaic system is predicted using LSTM and the decomposed time series. Meng et al.…”

Section: Related Workmentioning

confidence: 99%

Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction

Guo

Lai

Zhang

et al. 2023

IET Computer Vision

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“…The adaptive filter needed prior information about the environment around, which is also challenging to construct due to the mixed combination of sources and noise [ 18 , 19 ]. Wei Li et al [ 20 ] used variational mode decomposition (VMD) to process array signals aiming to reduce some effects. In this way, the author claimed, a signal composed by multiple sources can be decomposed into a series of quasi-orthogonal intrinsic mode functions (IMFs) non-recursively [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

Miao

Sun

et al. 2022

Sensors

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Utilizing the difference in phase and power spectrum between signals and noise, the estimation of direction of arrival (DOA) can be transferred to a spatial sample classification problem. The power ratio, namely signal-to-noise ratio (SNR), is highly required in most high-resolution beamforming methods so that high resolution and robustness are incompatible in a noisy background. Therefore, this paper proposes a Subspaces Deconvolution Vector (SDV) beamforming method to improve the robustness of a high-resolution DOA estimation. In a noisy environment, to handle the difficulty in separating signals from noise, we intend to initial beamforming value presets by incoherent eigenvalue in the frequency domain. The high resolution in the frequency domain guarantees the stability of the beamforming. By combining the robustness of conventional beamforming, the proposed method makes use of the subspace deconvolution vector to build a high-resolution beamforming process. The SDV method is aimed to obtain unitary frequency matrixes more stably and improve the accuracy of signal subspaces. The results of simulations and experiments show that when the input SNR is less than −27 dB, signals of decomposition differ unremarkably in the subspace while the SDV method can still obtain clear angles. In a marine background, this method works well in separating the noise and recruiting the characteristics of the signal into the DOA for subsequent processing.

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“…Multi-input deep convolutional neural networks have received high attention for their high algorithmic performance when they were proposed, and especially since the rise of deep learning, convolutional neural networks have once again attracted a lot of attention from researchers [ 11 ]. Accurate prediction of photovoltaic power output helps the power department to adjust the dispatching plan in a timely manner, coordinate the coordination of traditional power sources and photovoltaic power generation, meet the balance of power supply and demand, and ensure the reliable operation of the power grid [ 12 ]. Multi-input deep convolutional neural networks can use different convolutional kernels to extract different feature information of images, and its unique mechanism of local perceptual field and weight sharing can greatly reduce its network parameters and accelerate the training efficiency of the network [ 13 ].…”

Section: Introductionmentioning

confidence: 99%

Multi-Input Deep Convolutional Neural Network Model for Short-Term Power Prediction of Photovoltaics

Zhang¹,

Zhao²,

Kang

et al. 2022

Computational Intelligence and Neuroscience

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Along with the increasing prominence of energy and environmental issues, solar energy has received more and more extensive attention from countries around the world, and the installed capacity of photovoltaic power generation, as one of the main forms of solar energy development, has developed rapidly. Solar energy is by far the largest available source of energy on Earth, the use of solar power photovoltaic system has the advantages of flexible installation, simple maintenance, environmentally friendly, etc., by the world’s attention, especially the grid-connected photovoltaic power generation system has been rapid development. However, photovoltaic power generation itself is intermittent, affected by irradiance and other meteorological factors very drastically, and its own randomness and uncertainty are very large, and its grid connection affects the stability of the entire power grid. Therefore, the short-term prediction of photovoltaic power generation has important practical significance and guiding meaning. Multi-input deep convolutional neural networks belong to deep learning architectures, which use local connectivity, weight sharing, and subpolling operations, making it possible to reduce the number of weight parameters that need to be trained so that convolutional neural networks can perform well even with a large number of layers. In this paper, we propose a multi-input deep convolutional neural network model for PV short-term power prediction, which provides a short-term accurate prediction of PV power system output power, which is beneficial for the power system dispatching department to coordinate the cooperation between conventional power sources and PV power generation and reasonably adjust the dispatching plan, thus effectively mitigating the adverse effects of PV power system access on the power grid. Therefore, the accurate and reasonable prediction of PV power generation power is of great significance for the safe dispatch of power grid, maintaining the stable operation of power grid, and improving the utilization rate of PV power plants.

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Ultra-short-term Photovoltaic Power Prediction Based on VMD-LSTM-RVM Model

Cited by 15 publications

References 8 publications

Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction

Image feature learning combined with attention‐based spectral representation for spatio‐temporal photovoltaic power prediction

An Optimal Subspace Deconvolution Algorithm for Robust and High-Resolution Beamforming

Multi-Input Deep Convolutional Neural Network Model for Short-Term Power Prediction of Photovoltaics

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