Forecasting Short-Term Wind Speed Based on IEWT-LSSVM Model Optimized by Bird Swarm Algorithm

Xiang, Ling; Deng, Zeqi; Hu, Aijun

doi:10.1109/access.2019.2914251

Cited by 61 publications

(24 citation statements)

References 31 publications

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“…In the decomposition method, pre-processing techniques are applied to decompose the non-stationary time series data into stationary subseries. Decomposition approaches widely reported in the literature including Wavelet Transform (WT) [58], Wavelet Packet Decomposition (WPD) [76], Empirical Mode Decomposition (EMD) [72], variants of EMD including Ensemble EMD (EEMD) [77], Fast EEMD (FEEMD) [64], Complementary EEMD (CEEMD) [78], Complete EEMD with Adaptive Noise (CEEMDAN) [73], Improved CEEMDAN (ICEEM-DAN) [79], Intrinsic Time Scale Decomposition [80], Seasonal Adjustment Methods [81], Variational Mode Decomposition (VMD) [82], Optimized VMD (OVMD) [83], Empirical Wavelet Transform (EWT) [84], and Improved EWT (IEWT) [85]. There are two subtypes of decomposition: primary and secondary.…”

Section: ) Hybrid Methodsmentioning

confidence: 99%

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

2019

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The intermittent nature of wind energy raised multiple challenges to the power systems and is the biggest challenge to declare wind energy a reliable source. One solution to overcome this problem is wind energy forecasting. A precise forecast can help to develop appropriate incentives and wellfunctioning electric markets. The paper presents a comprehensive review of existing research and current developments in deterministic wind speed and power forecasting. Firstly, we categorize wind forecasting methods into four broader classifications: input data, timescales , power output, and forecasting method. Secondly, the performance of wind speed and power forecasting models is evaluated based on 634 accuracy tests reported in twenty-eight published articles covering fifty locations of ten countries. From the analysis, the most significant errors were witnessed for the physical models, whereas the hybrid models showed the best performance. Although, the physical models have a large normalized root mean square error values but have small volatility. The hybrid models perform best for every time horizon. However, the errors almost doubled at the medium-term forecast from its initial value. The statistical models showed better performance than artificial intelligence models only in the very short term forecast. Overall, we observed the increase in the performance of forecasting models during the last ten years such that the normalized mean absolute error and normalized root mean square error values reduced to about half the initial values.

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Section: ) Hybrid Methodsmentioning

confidence: 99%

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

2019

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“…Through the above three behaviors, the BSA has excellent convergence speed and search efficiency. Since proposed, the BSA has been applied in the field of wind speed prediction [35], Travel Salesman Problem (TSP) [36], distribution network planning [37], and target detection [38]. By combining the advantages of PSO and DE strategy and introducing Gaussian mutation through a certain frequency, the BSA has excellent global and local optimization capabilities.…”

Section: Related Workmentioning

confidence: 99%

Energy Management of a Power System for Economic Load Dispatch Using the Artificial Intelligent Algorithm

Zhang

Chao

2020

Electronics

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Economic Load Dispatch (ELD) is a key issue in power systems and its goal is to achieve minimum economic costs by allocating the output of generator units when satisfying the load demands and the operating constraints. As the dimension of the variables and the constraints increase, the traditional mathematical method is gradually not suitable for the ELD. This paper proposes an Improved Bird Swarm Algorithm (IBSA) to solve the ELD problem of a power system. By introducing the nonlinear cognitive and social coefficients, the proportion of individual learning and social learning of birds can be dynamically adjusted. In addition, the Levy flight strategy is added to the group between producers and beggars to increase the randomness. The performance of IBSA is verified via two systems consisting of 6 and 15 units, respectively, that take into account generation limitation, ramp rate limit, and prohibited operating zones. From the simulation results, the IBSA has shown excellent performance and robustness, which can be considered as a reliable solution for the ELD.

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“…Zhang et al used SVR to achieve bearing remaining life prediction [20]. Ling et al used Improved Empirical Wavelet Transform-Least Square Support Vector Machine (IEWT-LSSVM) and bird swarm algorithm to predict wind speed [21]. However, predicting bearing degradation is difficult owing to the non-linearity of bearing data.…”

Section: Introductionmentioning

confidence: 99%

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

Liu

et al. 2020

Sensors

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In real industrial applications, bearings in pairs or even more are often mounted on the same shaft. So the collected vibration signal is actually a mixed signal from multiple bearings. In this study, a method based on Hybrid Kernel Function-Support Vector Regression (HKF–SVR) whose parameters are optimized by Krill Herd (KH) algorithm was introduced for bearing performance degradation prediction in this situation. First, multi-domain statistical features are extracted from the bearing vibration signals and then fused into sensitive features using Kernel Joint Approximate Diagonalization of Eigen-matrices (KJADE) algorithm which is developed recently by our group. Due to the nonlinear mapping capability of the kernel method and the blind source separation ability of the JADE algorithm, the KJADE could extract latent source features that accurately reflecting the performance degradation from the mixed vibration signal. Then, the between-class and within-class scatters (SS) of the health-stage data sample and the current monitored data sample is calculated as the performance degradation index. Second, the parameters of the HKF–SVR are optimized by the KH (Krill Herd) algorithm to obtain the optimal performance degradation prediction model. Finally, the performance degradation trend of the bearing is predicted using the optimized HKF–SVR. Compared with the traditional methods of Back Propagation Neural Network (BPNN), Extreme Learning Machine (ELM) and traditional SVR, the results show that the proposed method has a better performance. The proposed method has a good application prospect in life prediction of coaxial bearings.

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Forecasting Short-Term Wind Speed Based on IEWT-LSSVM Model Optimized by Bird Swarm Algorithm

Cited by 61 publications

References 31 publications

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

Current Perspective on the Accuracy of Deterministic Wind Speed and Power Forecasting

Energy Management of a Power System for Economic Load Dispatch Using the Artificial Intelligent Algorithm

HKF-SVR Optimized by Krill Herd Algorithm for Coaxial Bearings Performance Degradation Prediction

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