Swarm Decomposition Technique Based Hybrid Model for Very Short-Term Solar PV Power Generation Forecast

Dokur, Emrah

doi:10.5755/j01.eie.26.3.25898

Cited by 15 publications

(9 citation statements)

References 18 publications

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“…where SX it (•) represents the fast Fourier transform of X it (n) and ω denotes the normalized signal frequency. e algorithm framework of SWD is summarized as follows [41]:…”

Section: Methodsmentioning

confidence: 99%

Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

Huang

Ran

et al. 2021

Shock and Vibration

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The acoustic-based internal defect detection is essential to ensure the quality of arc magnets efficiently. Swarm decomposition (SWD) is conducive to processing acoustic signals, but it is still confronted with threshold optimization problems. Especially, the existing optimization methods for the SWD thresholds are merely available for a single signal with exclusive characteristics, instead of the various signals with similar characteristics. Therefore, a threshold-optimized SWD using grey wolf optimizer (GWO) is proposed to solve these issues and applied to detect the internal defects of arc magnets. In this method, a fitness function is designed to indicate the relationship between the SWD thresholds and the overall decomposition effect of similar signals. The minimum value of it corresponds to the threshold setting yielding the optimal decomposition. GWO is used for searching such a minimum value, and the obtained optimal threshold setting allows SWD to decompose any signal into a series of oscillatory components. The frequency information in the two oscillatory components with the highest energy ratio is extracted as the internal defect features. Random forest is carried out to identify these features. Experimentally, the detection accuracy reaches above 97%, and the detection speed per single arc magnet does not exceed 3.4 seconds. The proposed method cannot only determine the unified threshold setting of SWD for similar signals but also achieve an accurate, rapid detection for the internal defects.

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“…where SX it (•) represents the fast Fourier transform of X it (n) and ω denotes the normalized signal frequency. e algorithm framework of SWD is summarized as follows [41]:…”

Section: Methodsmentioning

confidence: 99%

Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

Huang

Ran

et al. 2021

Shock and Vibration

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show abstract

“…Moreover, selecting a main wavelet function in wavelet-based decomposition methods is still fairly challenging. Swarm decomposition (SWD) [34] has been shown to be effective in dealing with the mode mix-ing problem of EMD for synthetic and real-time applications in several recent studies, including [17,35,36]. Given these advantages, the SWD was selected to decompose wave time series data in this study.…”

Section: Introductionmentioning

confidence: 99%

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

Dokur,

Erdogan,

Salari

et al. 2024

IET Renewable Power Gen

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While wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current experience in wave energy systems demonstrates that operation and maintenance costs are dominant in their cost structure due to unplanned maintenance resulting in energy production loss. Accurate and high performance simulation forecasting tools are required to improve the efficiency and safety of wave converters. This paper proposes a new methodology for significant wave height forecasting. It is based on incorporating swarm decomposition (SWD) and multi‐strategy random weighted grey wolf optimizer (MsRwGWO) into a multi‐layer perceptron (MLP) forecasting model. This approach takes advantage of the SWD approach to generate more stable, stationary, and regular patterns of the original signal, while the MsRwGWO optimizes the MLP model parameters efficiently. As such, forecasting accuracy has improved. Real wave datasets from three buoys in the North Atlantic Sea are used to test and validate the forecasting performance of the proposed model. Furthermore, the performance is evaluated through a comparison analysis against deep‐learning based state‐of‐the‐art forecasting models. The results show that the proposed approach significantly enhances the model's accuracy.

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“…In this regard, swarm decomposition (SWD) algorithms based on swarm-prey hunting approach can intelligently decompose the high-frequency components. While the SWD has been used in several recent forecasting studies, including solar [21] and offshore wind [22], the use of SWD as a multi-decomposition tool is worth investigating in order to have more stable sub-series components of the signal. As such, the model performance can be improved.…”

Section: Introductionmentioning

confidence: 99%

EV Fleet Charging Load Forecasting Based on Multiple Decomposition With CEEMDAN and Swarm Decomposition

2022

Self Cite

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As the transition to electric mobility is accelerating, EV fleet charging loads are expected to become increasingly significant for power systems. Hence, EV fleet load forecasting is vital to maintaining the reliability and safe operation of the power system. This paper presents a new multiple decomposition based hybrid forecasting model for EV fleet charging. The proposed approach incorporates the Swarm Decomposition (SWD) into the Complete Ensemble Empirical Mode Decomposition Adaptive Noise (CEEMDAN) method. The multiple decomposition approach offers more stable, stationary, and regular features of the original signals. Each decomposed signal is fed into artificial intelligence based forecasting models including multi-layer perceptron (MLP), long short-term memory (LSTM) and bidirectional LSTM (Bi-LSTM). Real EV fleet charging data sets from the field are used to validate the performance of the models. Various statistical metrics are used to quantify the prediction performance of the proposed model through a comparative analysis of the implemented models. It is demonstrated that the multiple decomposition approach improved the model performance with an R 2 value increasing from 0.8564 to 0.9766 as compared to the models with single decomposition.

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Swarm Decomposition Technique Based Hybrid Model for Very Short-Term Solar PV Power Generation Forecast

Cited by 15 publications

References 18 publications

Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

Threshold‐Optimized Swarm Decomposition Using Grey Wolf Optimizer for the Acoustic‐Based Internal Defect Detection of Arc Magnets

An integrated methodology for significant wave height forecasting based on multi‐strategy random weighted grey wolf optimizer with swarm intelligence

EV Fleet Charging Load Forecasting Based on Multiple Decomposition With CEEMDAN and Swarm Decomposition

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