Improved prediction method of PV output power based on optimised chaotic phase space reconstruction

Wang, Yufei; Fu, Yuxi; Xue, Hong

doi:10.1049/iet-rpg.2019.0809

Cited by 17 publications

(11 citation statements)

References 26 publications

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“…Considering that the PV power generation system is a non‐linear complex system, comprehensive consideration of external factors will increase the difficulty of prediction. By reconstructing the chaotic phase space of one‐dimensional PV power time series, the hidden regularity of chaotic attractors can be mapped in high‐dimensional phase space and the difficulty of PV power forecasting can be effectively reduced [26, 27].…”

Section: Gwo‐eenn Prediction Model Of Pv Power Based On Hp‐ovmdmentioning

confidence: 99%

Ultra‐short‐term PV power prediction model based on HP‐OVMD and enhanced emotional neural network

Wang

Yang

Xue

et al. 2022

IET Renewable Power Gen

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Accurate photovoltaic (PV) power prediction plays an increasingly crucial role to maintain the safety and reliability of power grid operation. However, the fluctuation and nonstationarity of PV power make it a challenging task to optimize the accurate results. This paper presents a novel prediction model which is the combination of Hodrick-Prescott (HP) filter, optimized variational mode decomposition (OVMD), and enhanced emotional neural network (EENN). It overcomes the adverse effects of random changes under highly volatile weather conditions. First, the trend component and fluctuation component of PV power are screened through HP filter as the pre-step to alleviate the non-linearity impact of PV power data. Then, OVMD is used to decompose the residual PV power time series into a series of relatively stationary intrinsic modes. Finally, the EENN model optimized by the grey wolf optimization (GWO) is established to predict each subseries, and the prediction results of each subseries are reconstructed to obtain the final predicted results. The numerical results based on actual PV power data show that the prediction accuracy of the proposed model is significantly improved compared with the contrast models, and the proposed model achieves the best accuracy against the OVMD-GWO-EENN, VMD-GWO-EENN, and GWO-EENN models.

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Section: Gwo‐eenn Prediction Model Of Pv Power Based On Hp‐ovmdmentioning

confidence: 99%

Ultra‐short‐term PV power prediction model based on HP‐OVMD and enhanced emotional neural network

Wang

Yang

Xue

et al. 2022

IET Renewable Power Gen

Self Cite

View full text Add to dashboard Cite

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“…Nie et al (2020) proposed a two-stage classification-prediction framework for predicting contemporaneous PV power output from sky images and compared it with an end-to-end convolution neural network [47]. presented an improved solar output power prediction method based on optimised chaotic phase space reconstruction [48]. Erduman (2020) developed an artificial neural network-based model for solar PV output power prediction [49].…”

Section: Plos Onementioning

confidence: 99%

“…• Occurrences of global minima and stagnation issues [3][4][5][6][7] • Scalability problems on the normalization procedures adopted [2,8,[12][13][14][15][16][17] • Over-fitting and under-fitting issues [5, 6, 9-11, 23, 48, 51] • Dimensionality constraints of the solar farm data and data handling issues [18][19][20][21][22][23][24] • Elapsed training time [29,31,37] • Data extraction problems in regression based ML models [10][11][12][13][14][15] • Higher number of trainable parameters in DL models [1, 14, 19-20, 26, 27, 43, 47] • Repetitive training of deep neural networks [19,20,26,27] • High computational overhead due to repetitive process [29][30][31][32][33][34][35][36] • Few predictor models with high complexity and data redundancy [45][46][47]…”

Section: Challengesmentioning

confidence: 99%

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

et al. 2022

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A novel Variational Mode Decomposition (VMD) combined Fuzzy-Twin Support Vector Machine Model with deep learning mechanism is devised in this research study to forecast the solar Photovoltaic (PV) output power in day ahead basis. The raw data from the solar PV farms are highly fluctuating and to extract the useful stable components VMD is employed. A novel Fuzzy–Twin Support Vector Machine (FTSVM) model developed acts as the forecasting model for predicting the solar PV output power for the considered solar farms. The twin support vector machine (SVM) model formulates two separating hyperplanes for predicting the output power and in this research study a fuzzy based membership function identifies most suitable two SVM prediction hyperplanes handling the uncertainties of solar farm data. For the developed, new VMD-FTSVM prediction technique, their optimal parameters for the training process are evaluated with the classic Ant Lion Optimizer (ALO) algorithm. The solar PV output power is predicted using the novel VMD-FTSVM model and during the process multi-kernel functions are utilized to devise the two fuzzy based hyperplanes that accurately performs the prediction operation. Deep learning (DL) based training of the FTSVM model is adopted so that the deep auto-encoder and decoder module enhances the accuracy rate. The proposed combined forecasting model, VMD-ALO-DLFTSVM is validated for superiority based on a two 250MW PV solar farm in India. Results prove that the proposed model outperforms the existing model in terms of the performance metrics evaluated and the forecasted PV Power.

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“…Consequently, researchers have amalgamated random search algorithms with neural network technology to construct prediction models that enhance the computational efficiency and search capacity of conventional prediction models. For instance, Wang et al [21] introduced an enhanced photovoltaic power prediction method grounded in optimized chaotic phase space reconstruction. This model incorporated chaotic theory and empirical mode decomposition (EEMD) to discern the fluctuating characteristics of photovoltaic output power.…”

Section: Introductionmentioning

confidence: 99%

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

Huang,

Guo,

Wei

et al. 2024

IET Renewable Power Gen

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In response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement weather conditions, this research article introduces a concise and effective method for short‐term power prediction of PVBSs when subjected to non‐sunny weather conditions, leveraging the paradigm of software defined networking. This method ingeniously amalgamates the improved Northern Goshawk optimized back propagation neural network (INGO‐BP) and recurrent generative adversarial networks (RGAN). The investigation commences by delineating a comprehensive PVBS system framework underpinned by SDN principles. Subsequently, it employs the Pearson correlation coefficient technique and principal component analysis for the judicious selection of pertinent features from the PVBS photovoltaic dataset, thereby ameliorating the issue of multicollinearity within the predictive model. To culminate the research endeavour, the INGO algorithm is enlisted to optimize the weightings and bias parameters of the BP neural network, while RGAN is harnessed for the acquisition of distinctive distribution patterns in photovoltaic data across diverse meteorological conditions. This augmentation enhances the model's capacity to discern the intricate long‐term mappings between photovoltaic power and meteorological data. The empirical findings substantiate the elevated accuracy of the INGO‐BP prediction model across sunny, cloudy, and rainy weather conditions, with a discernible enhancement in predictive accuracy for PVBS performance under non‐sunny weather conditions facilitated by data augmentation via RGAN.

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Improved prediction method of PV output power based on optimised chaotic phase space reconstruction

Cited by 17 publications

References 26 publications

Ultra‐short‐term PV power prediction model based on HP‐OVMD and enhanced emotional neural network

Ultra‐short‐term PV power prediction model based on HP‐OVMD and enhanced emotional neural network

Variational mode decomposition combined fuzzy—Twin support vector machine model with deep learning for solar photovoltaic power forecasting

Short‐term power forecasting method for 5G photovoltaic base stations on non‐sunny days based on SDN‐integrated INGO‐BP and RGAN

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