An improved neural network-based approach for short-term wind speed and power forecast

Chang, G.W.; Lu, H. J.; Chang, Yung‐Ruei; Lee, Y.D.

doi:10.1016/j.renene.2016.12.071

“…These prediction methods usually use historical data, through some linear models include autoregressive moving average model (ARMA) [9,34], autoregressive integrated moving average model (ARIMA) [2]. The nonlinear model include SVM [8,12], LSSVM [36,39], artificial neural network (Elman neural network [44,45], echo state network [38], fuzzy neural network [6,30], RBF neural network [4,23], and etc to predict short-term wind speed. The results of some related literatures indicate that the short-term wind speed has strong nonlinearity [1,24], so the nonlinear model is more suitable for shortterm wind speed prediction.…”

Section: Review Of Short-term Wind Speed Predictionmentioning

confidence: 99%

Patient-Adapted and Inter-Patient Ecg Classification Using Neural Network and Gradient Boosting

Zaorálek¹,

Platoš²,

Snåšel³

2018

NNW

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Abstract:As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction perfromance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.

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“…These prediction methods usually use historical data, through some linear models include autoregressive moving average model (ARMA) [9,34], autoregressive integrated moving average model (ARIMA) [2]. The nonlinear model include SVM [8,12], LSSVM [36,39], artificial neural network (Elman neural network [44,45], echo state network [38], fuzzy neural network [6,30], RBF neural network [4,23], and etc to predict short-term wind speed. The results of some related literatures indicate that the short-term wind speed has strong nonlinearity [1,24], so the nonlinear model is more suitable for shortterm wind speed prediction.…”

Section: Review Of Short-term Wind Speed Predictionmentioning

confidence: 99%

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

Nabovati¹,

Haleh²,

Vahdani³

2018

NNW

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Abstract:As an improved algorithm of standard extreme learning machine, online sequential extreme learning machine achieves excellent classification and regression performance. However, online sequential extreme learning machine gives the same weight to the old and new training samples, and fails to highlight the importance of the new training samples. At the same time, the algorithm updates the network weights after obtaining the new training samples. This network weight updating mode lacks flexibility and increases unnecessary computation. This paper proposes an adaptive online sequential extreme learning machine with an effective sample updating mechanism. The new and old samples are given different weights. The effect of new training samples on the algorithm is further enhanced, which can further improve the regression prediction ability of extreme learning machine. At the same time, an improved artificial bee colony algorithm is proposed and used to optimize the parameters of the adaptive online sequential extreme learning machine. The stability and convergence property of proposed prediction method are proved. The actual collected short-term wind speed time series is used as the research object and verify the prediction perfromance of the proposed method. Multi step prediction simulation of short-term wind speed is performed out. Compared with other prediction methods, the simulation results show that the proposed approach has higher prediction accuracy and reliability performance, meanwhile improve the performance indicators.

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“…As one kind of the rapidly growing renewable energy sources, wind energy has been recognized as an attractive alternative to conventional fossil fuels due to several advantages, including renewability and pollution-free environment [2]. However, wind power is recognized as a stochastic process [3] because of the intermittent and multi-scale characteristics of wind speed fluctuation [4,5].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

Wang

¹

,

Wang

²

,

Li

³

2018

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Abstract:With the growing penetration of wind power into electric grids, improving wind speed prediction accuracy has become particularly valuable for the exploitation of wind power. In this paper, a novel hybrid strategy based on a three-phase signal decomposition (TPSD) technique, feature extraction (FE) and weighted regularized extreme learning machine (WRELM) is developed for multi-step ahead wind speed prediction. The TPSD including seasonal separation algorithm (SSA), fast ensemble empirical mode decomposition (FEEMD) and variational mode decomposition (VMD) is proposed for the first time to handle the complex and irregular natures of wind speed comprehensively. The FE process is used to capture the useful features of wind speed fluctuations and determine the optimal inputs for a prediction model. The WRELM is employed as a basic predictor for building the prediction model by these selected features. Four real wind speed prediction cases are utilized to evaluate the proposed model, and experimental results verify the effectiveness of the proposed model compared with the benchmark models.

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An improved neural network-based approach for short-term wind speed and power forecast

Cited by 226 publications

References 23 publications

Patient-Adapted and Inter-Patient Ecg Classification Using Neural Network and Gradient Boosting

Patient-Adapted and Inter-Patient Ecg Classification Using Neural Network and Gradient Boosting

Fuzzy Multi-Objective Optimization Algorithms for Solving Multi-Mode Automated Guided Vehicles by Considering Machine Break Time and Artificial Neural Network

A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction

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