Serial-parallel dynamic echo state network: A hybrid dynamic model based on a chaotic coyote optimization algorithm for wind speed prediction

Ding, Lei; Bai, Yulong; Fan, Manhong; Yu, Qinghe; Zhu, Ya-Jie; Chen, Xiao-Yao

doi:10.1016/j.eswa.2022.118789

Cited by 26 publications

(6 citation statements)

References 43 publications

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“…Combining all of the subsequence predictions yields the ultimate prediction value. The study [41] proposes a serial-parallel dynamic network with dual dynamical features. The research develops a dynamic prediction model based on phase space reconstruction that incorporates the chaotic Coyote optimization algorithm and the enhanced complete ensemble EMD with the adaptive noise method.…”

Section: -Related Workmentioning

confidence: 99%

A hybrid model based on Discrete wavelet prediction (DWT), Bidirectional recurrent neural networks to wind speed prediction

Barjasteh

Ghafouri

Hashemipour

2023

Preprint

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Wind speed is the main driver of wind power output, but its inherent fluctuations and deviations present significant challenges for power system security and power quality. Accurate short-term wind power forecasting is necessary to ensure the stability and integration of wind energy into the grid. Non-stationarity is a major challenge in analyzing wind speed data, and change-point detection are essential for optimal resource allocation. This paper addresses the issue of short-term wind power forecasting for stable and effective wind energy system operation. To predict non-stationary data and detect change points, non-stationary data must first be transformed into stationary data. Discrete wavelet transformation (DWT) is used to decompose wind speed traces into low- and high-frequency components for more accurate predictions using deep learning algorithms. The proposed approach uses a Gated Recurrent Unit (GRU) network, which has a concise network structure and requires less computational load, making it suitable for quickly predicting short-term and long-term dependencies in wind speed data. Experiments demonstrate that the proposed method outperforms other cutting-edge methods in terms of prediction accuracy.

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Section: -Related Workmentioning

confidence: 99%

A hybrid model based on Discrete wavelet prediction (DWT), Bidirectional recurrent neural networks to wind speed prediction

Barjasteh

Ghafouri

Hashemipour

2023

Preprint

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“…However, the parallel system cannot automatically correct the prediction inaccuracy of the serial structure. An improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) approach, the chaotic coyote optimization algorithm (CCOA) and the SP-ESN, are then included in a dynamic forecast model created on stage space reconstruction [49]. This is a helpful model that considers the impact of several climatological variables to advance the accuracy of short-term wind speed forecasts.…”

Section: Hybrid Approachesmentioning

confidence: 99%

A Comprehensive Review on Machine Learning Techniques for Forecasting Wind Flow Pattern

Sri Preethaa,

Muthuramalingam,

Natarajan

et al. 2023

Sustainability

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The wind is a crucial factor in various domains such as weather forecasting, the wind power industry, agriculture, structural health monitoring, and so on. The variability and unpredictable nature of the wind is a challenge faced by most wind-energy-based sectors. Several atmospheric and geographical factors influence wind characteristics. Many wind forecasting methods and tools have been introduced since early times. Wind forecasting can be carried out short-, medium-, and long-term. The uncertainty factors of the wind challenge the accuracy of techniques. This article brings the general background of physical, statistical, and intelligent approaches and their methods used to predict wind characteristics and their challenges—this work’s objective is to improve effective data-driven models for forecasting wind-power production. The investigation and listing of the effectiveness of improved machine learning models to estimate univariate wind-energy time-based data is crucially the prominent focus of this work. The performance of various ML predicting models was examined using ensemble learning (ES) models, such as boosted trees and bagged trees, Support Vector Regression (SVR) with distinctive kernels etc. Numerous neural networks have recently been constructed for forecasting wind speed and power due to artificial intelligence (AI) advancement. Based on the model summary, further directions for research and application developments can be planned.

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“…However, there is little information available on SCUAV by the hybrid solar irradiance prediction model; its accurate prediction is advantageous for energy management and mission planning. For wind prediction, numerous studies have been undertaken on prediction models, including persistence [39], classical statistical [40] and hybrid model forecasting [41,42]. The hybrid prediction method improves on the historical data method's shortcomings to account for the influence of future atmospheric movement and other factors, as well as the single numerical weather forecast's lack of accurate description of the target area due to the influence of model, time, and topographic space.…”

Section: Introductionmentioning

confidence: 99%

State-of-Charge Trajectory Planning for Low-Altitude Solar-Powered Convertible UAV by Driven Modes

Cao,

Liu

2024

Drones

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The conversion efficiency of solar energy and the capacity of energy storage batteries limit the development of low-altitude solar-powered aircrafts in the face of challenging meteorological phenomena in the lower atmosphere. In this paper, the energy planning problem of solar-power convertible unmanned aerial vehicles (SCUAVs) is studied, and a degressive state-of-charge (SOC) trajectory planning method with energy management strategy (EMS) is proposed. The SOC trajectory planning strategy is divided into four stages driven by three modes, which achieves the energy cycle of SCUAV’s long-endurance cruise and multiple hovers without the need to fully charge the battery SOC. The EMS is applied to control the output of solar cell/battery and power distribution for each stage according to three modes. A prediction model based on wavelet transform (WT), long short-term memory (LSTM) networks and autoregressive integrated moving average (ARIMA) is proposed for the weather forecast in the low altitude, where solar irradiance is used for the prediction of solar input power, and the wind and its inflow direction take into account the multi-mode power prediction. Numerical and simulation results indicate that the effectiveness of the proposed SOC trajectory planning method has a positive impact on low-altitude solar-powered aircrafts.

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Serial-parallel dynamic echo state network: A hybrid dynamic model based on a chaotic coyote optimization algorithm for wind speed prediction

Cited by 26 publications

References 43 publications

A hybrid model based on Discrete wavelet prediction (DWT), Bidirectional recurrent neural networks to wind speed prediction

A hybrid model based on Discrete wavelet prediction (DWT), Bidirectional recurrent neural networks to wind speed prediction

A Comprehensive Review on Machine Learning Techniques for Forecasting Wind Flow Pattern

State-of-Charge Trajectory Planning for Low-Altitude Solar-Powered Convertible UAV by Driven Modes

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