Short-Term Solar Power Forecasting Using Random Vector Functional Link (RVFL) Network

Aggarwal, Arpit; Tripathi, M. M.

doi:10.1007/978-981-10-7386-1_3

Cited by 8 publications

(8 citation statements)

References 9 publications

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“…RVFL has a better training speed and can be updated quickly, and it also has fine nonlinear fitting ability [45]. Hence it has been widely used in various fields including energy related forecasting [18,46].…”

Section: Random Vector Functional Link (Rvfl) Neural Networkmentioning

confidence: 99%

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Miu

2020

Energies

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Crude oil is one of the strategic energies and plays an increasingly critical role effecting on the world economic development. The fluctuations of crude oil prices are caused by various extrinsic and intrinsic factors and usually demonstrate complex characteristics. Therefore, it is a great challenge for accurately forecasting crude oil prices. In this study, a self-optimizing ensemble learning model incorporating the improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), sine cosine algorithm (SCA), and random vector functional link (RVFL) neural network, namely ICEEMDAN-SCA-RVFL, is proposed to forecast crude oil prices. Firstly, we employ ICEEMDAN to decompose the raw series of crude oil prices into a group of relatively simple subseries. Secondly, RVFL is used to forecast the target values for each decomposed subseries individually. Due to the complex parameter settings of ICEEMDAN and RVFL, SCA is introduced to optimize the parameters for ICEEMDAN and RVFL in the above decomposition and prediction stages simultaneously. Finally, we assemble the predicted values of all individual subseries as the final predicted values of crude oil prices. Our proposed ICEEMDAN-SCA-RVFL significantly outperforms the single and ensemble benchmark models, as demonstrated by a case study conducted using the time series of West Texas Intermediate (WTI) daily crude oil spot prices.

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Section: Random Vector Functional Link (Rvfl) Neural Networkmentioning

confidence: 99%

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Miu

2020

Energies

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“…With renewable energy development, solar power forecasting is an emerging area. In [174], the authors compare RVFL with SLFN and RWSLFN, and the results demonstrate the superiority of the direct link. Signal decomposition methods also work for solar time series.…”

Section: Solar Powermentioning

confidence: 99%

“…Accurate and reliable forecasts help the stakeholders and decision-makers plan, organize, maintain and develop the system in advance in a data-driven fashion. RVFL and the improved versions have demonstrated their outstanding performance on various forecasting tasks from different domains, such as electricity load [105,143], solar power [174], wind power [148], financial time series [133] and other data [108]. This section presents the details of all the literature about RVFL-based forecasting.…”

Section: Other Applications Of Rvfl Modelmentioning

confidence: 99%

Random vector functional link network: recent developments, applications, and future directions

Malik¹,

Gao²,

Ganaie³

et al. 2022

Preprint

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Neural networks have been successfully employed in various domain such as classification, regression and clustering, etc. Generally, the back propagation (BP) based iterative approaches are used to train the neural networks, however, it results in the issues of local minima, sensitivity to learning rate and slow convergence. To overcome these issues, randomization based neural networks such as random vector functional link (RVFL) network have been proposed. RVFL model has several characteristics such as fast training speed, simple architecture, and universal approximation capability, that make it a viable randomized neural network. This article presents the comprehensive review of the development of RVFL model, which can serve as the extensive summary for the beginners as well as practitioners. We discuss the shallow RVFL, ensemble RVFL, deep RVFL and ensemble deep RVFL models. The variations, improvements and applications of RVFL models are discussed in detail. Moreover, we discuss the different hyperparameter optimization techniques followed in the literature to improve the generalization performance of the RVFL model. Finally, we give potential future research directions/opportunities that can inspire the researchers to improve the RVFL architecture further.

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“…We have only found two references applying RVFL approaches to solar energy prediction problems. The work in [92] proposes a RVFL network for shortterm solar power forecasting, and compares it with other two artificial neural systems: the single shrouded layer feed-forward neural network (SLFN) and the random weight single shrouded layer feed-forward neural network (RWSLFN). Data for training and evaluating were adquired from the solar power data of Sydney, Australia.…”

Section: Rvfl Network In Solar Energy Predictionmentioning

confidence: 99%

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

Ser¹,

Casillas-Pérez²,

Cornejo-Bueno³

et al. 2021

Preprint

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Randomization-based Machine Learning methods for prediction are currently a hot topic in Artificial Intelligence, due to their excellent performance in many prediction problems, with a bounded computation time. The application of randomization-based approaches to renewable energy prediction problems has been massive in the last few years, including many different types of randomization-based approaches, their hybridization with other techniques and also the description of new versions of classical randomization-based algorithms, including deep and ensemble approaches. In this paper we review the most important characteristics of randomization-based machine learning approaches and their application to renewable energy prediction problems. We describe the most important methods and algorithms of this family of modeling methods, and perform a critical literature review, examining predic-

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Short-Term Solar Power Forecasting Using Random Vector Functional Link (RVFL) Network

Cited by 8 publications

References 9 publications

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Daily Crude Oil Price Forecasting Based on Improved CEEMDAN, SCA, and RVFL: A Case Study in WTI Oil Market

Random vector functional link network: recent developments, applications, and future directions

Randomization-based Machine Learning in Renewable Energy Prediction Problems: Critical Literature Review, New Results and Perspectives

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