Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements

Wang, Meng; Peng, Jinqing; Luo, Yimo; Shen, Zhicheng; Yang, Hongxing

doi:10.1016/j.energy.2021.120162

Cited by 62 publications

(20 citation statements)

References 38 publications

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“…Lee et al (2021) explored the probabilistic approach neural model to improve the prediction of the photovoltaic rate of power output per hour [37]. tested the energy outputs of different types of PV modules and computed the accuracies of various simplistic PV module power prediction models [38]. Wang and Shi (2021) improved the ability of short-term solar radiation prediction using sparse subspace representation and k-nearest-neighbour approach [39].…”

Section: Plos Onementioning

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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Section: Plos Onementioning

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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“…The standard mathematical model for the PV cell is not reliable, but it is enough to understand the phenomenon within the PV cell parameters during working hours [20]. The mathematical model with series resistance works well for the crystalline module but shows an error when modelling the slim film innovation module [21]. It is concluded from the previous literature that most mathematical models have used unknown parameters like series and shunt resistance inputs.…”

Section: Review On Pv Cell and Related Modelling Approachesmentioning

confidence: 99%

An improved model and performance analysis for grid-connected photovoltaic system in Oman

Al-Shahri

Ismail

Al-Muhsen

et al. 2022

Heliyon

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Section: Introductionmentioning

confidence: 99%

“…The prediction of the performance of PV system installations under real operating conditions has been the subject of several studies [10]- [14]. Studies have proposed empirical laws expressing the variation of PV module performances, such as maximum power (Pmax), VOC, ICC and conversion efficiency, as a function of TM and G [10]. Other works introduce correction coefficients into these laws that take into account the effects of other factors on PV module performance, among which are incidence angle and solar spectral distribution [15].…”

Section: Introductionmentioning

confidence: 99%

Prediction of the Performance of Photovoltaic Modules from the Behavior of Physical Parameters According to Irradiance and Temperature

2023

IJIES

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In this paper, a study on photovoltaic module physical parameters behavior depending on irradiance G and module temperature TM is presented. From this investigation, new equations translating physical parameters behavior depending on TM and G are developed. The new proposed laws, which combine the effects of G and TM in a correlated manner, have the advantage of incorporating G effect on the temperature coefficients by including specific terms. The developed laws form a basis for predicting PV module performance and can be incorporated into maximum power point tracking (MPPT) programs to extract peak power output under different operating conditions. This study is conducted by examining the electrical performances at the maximum power (Im, Vm), the open circuit (0, VOC), and the short circuit (ISC, 0) points measured using an elaborate experimental platform. The photovoltaic module used is a monocrystalline type exposed to four G values of 700, 800, 880 and 1000 W.m -2 for temperatures TM between 16°C and 48°C.

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Comparison of different simplistic prediction models for forecasting PV power output: Assessment with experimental measurements

Cited by 62 publications

References 38 publications

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

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

An improved model and performance analysis for grid-connected photovoltaic system in Oman

Prediction of the Performance of Photovoltaic Modules from the Behavior of Physical Parameters According to Irradiance and Temperature

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