Modeling uncertain processes with interval random vector functional-link networks

Guan, Shouping; Cui, Zhouying

doi:10.1016/j.jprocont.2020.07.008

Cited by 5 publications

(1 citation statement)

References 33 publications

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“…Igelnik and Pao (1995) proved that RVFLN with random parameters chosen from the uniform distribution defined over a range can be a universal approximator with probability one for continuous functions. RVFLN has been applied to industrial processes for modeling (Guan & Cui, 2020) and estimation (Dai et al, 2019). At present, an advanced randomized neural network, named stochastic configuring networks (SCNs), was proposed in Wang and Li (2017).…”

Section: Introductionmentioning

confidence: 99%

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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