Modeling the forecasted power of a photovoltaic generator using numerical weather prediction and radiative transfer models coupled with a behavioral electrical model

Razagui, Abdelhak; Abdeladim, K.; Semaoui, Smail; Arab, A. Hadj; Boulahchiche, Saliha

doi:10.1016/j.egyr.2019.08.018

Cited by 9 publications

(3 citation statements)

References 9 publications

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“…Due to the fact that PV power generation will change with time and environment, PV power generation has obvious periodicity, volatility and discontinuity (Razagui et al, 2020). There are many factors that affect the power generation of PV system.…”

Section: Analysis Of Volt Ampere Characteristics Of Pv Cellsmentioning

confidence: 99%

“…The PV power generation prediction models can be divided into physical methods, persistence methods and statistical methods (Barbieri et al, 2017). The physical methods focus on identifying factors that affect the PV power generation (Razagui et al, 2020; Sanjari et al, 2020). Persistence methods usually assume a strong correlation between current and future values (Fu, 2018).…”

Section: Introductionmentioning

confidence: 99%

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Short‐term photovoltaic power forecasting with adaptive stochastic configuration network ensemble

Guo

Wang

et al. 2022

WIREs Data Min & Knowl

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The volatility and intermittency of solar energy seriously restrict the development of the photovoltaic (PV) industry. Accurate forecast of short-term PV power generation is essential for the optimal balance and dispatch of power plants in the smart grid. This article presents a machine learning approach for analyzing the volt-ampere characteristics and influential factors on PV data. A correlation analysis is employed to discover some hidden characteristic variables. Then, an adaptive ensemble method with stochastic configuration networks as base models (AE-SCN) is proposed to construct the PV prediction model, which integrates bagging and adaptive weighted data fusion algorithms. Compared with the original SCN, SCN ensemble (SCNE) and random vector functional-link network (RVFLN), linear regression model, random forest model and autoregressive integrated moving average (ARMA) model, AE-SCN performs favorably in the terms of the prediction accuracy.

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Section: Analysis Of Volt Ampere Characteristics Of Pv Cellsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Short‐term photovoltaic power forecasting with adaptive stochastic configuration network ensemble

Guo

Wang

et al. 2022

WIREs Data Min & Knowl

View full text Add to dashboard Cite

show abstract

“…The physical models use NWP models or satellite imagery alongside physical considerations such as meteorological or topological data. However, physical models are restricted to tedious mathematical approaches for specific PV plants, leading to poor generalization potential and complicated modeling [32,34]. Statistical models employ prediction models such as Moving Average (MA) and Autoregressive (AR) [35].…”

Section: Literature Reviewmentioning

confidence: 99%

Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

et al. 2021

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Short-term Photovoltaic (PV) Power Forecasting (STPF) is considered a topic of utmost importance in smart grids. The deployment of STPF techniques provides fast dispatching in the case of sudden variations due to stochastic weather conditions. This paper presents an efficient data-driven method based on enhanced Random Forest (RF) model. The proposed method employs an ensemble of attribute selection techniques to manage bias/variance optimization for STPF application and enhance the forecasting quality results. The overall architecture strategy gathers the relevant information to constitute a voted feature-weighting vector of weather inputs. The main emphasis in this paper is laid on the knowledge expertise obtained from weather measurements. The feature selection techniques are based on local Interpretable Model-Agnostic Explanations, Extreme Boosting Model, and Elastic Net. A comparative performance investigation using an actual database, collected from the weather sensors, demonstrates the superiority of the proposed technique versus several data-driven machine learning models when applied to a typical distributed PV system.

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Application of ANN–ANFIS Model for Forecasting Solar Power

Perveen,

Anand,

Kumar

2024

Photovoltaic Systems Technology

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Modeling the forecasted power of a photovoltaic generator using numerical weather prediction and radiative transfer models coupled with a behavioral electrical model

Cited by 9 publications

References 9 publications

Short‐term photovoltaic power forecasting with adaptive stochastic configuration network ensemble

Short‐term photovoltaic power forecasting with adaptive stochastic configuration network ensemble

Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements

Application of ANN–ANFIS Model for Forecasting Solar Power

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