A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting

Li, Chaoshun; Xiao, Zhao; Xia, Xin; Zou, Wen; Zhang, Chu

doi:10.1016/j.apenergy.2018.01.094

“…For the sake of comparison, the multi-objective optimization and traditional single objective optimization methods are presented. A suitable optimization strategy and algorithm may play a vital role in solving complicated optimization problems [22,23]. MOPSO [12,24] is applied for multi-objective optimization, and Particle Swarm Optimization (PSO) [25] is used for single objective optimization.…”

Section: Multi-objective Optimization Of Start-up Strategymentioning

confidence: 99%

Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

Hou

¹

,

Li

²

,

Tian

³

et al. 2018

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This paper proposes a multi-objective optimization method for the start-up strategy of pumped storage units (PSU) for the first time. In the multi-objective optimization method, the speed rise time and the overshoot during the process of the start-up are taken as the objectives. A precise simulation platform is built for simulating the transient process of start-up, and for calculating the objectives based on the process. The Multi-objective Particle Swarm Optimization algorithm (MOPSO) is adopted to optimize the widely applied start-up strategies based on one-stage direct guide vane control (DGVC), and two-stage DGVC. Based on the Pareto Front obtained, a multi-objective decision-making method based on the relative objective proximity is used to sort the solutions in the Pareto Front. Start-up strategy optimization for a PSU of a pumped storage power station in Jiangxi Province in China is conducted in experiments. The results show that: (1) compared with the single objective optimization, the proposed multi-objective optimization of start-up strategy not only greatly shortens the speed rise time and the speed overshoot, but also makes the speed curve quickly stabilize; (2) multi-objective optimization of strategy based on two-stage DGVC achieves better solution for a quick and smooth start-up of PSU than that of the strategy based on one-stage DGVC.

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“…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. But the key parameters of SVM and LSSVM have no definite determination method.…”

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.

show abstract

“…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. But the key parameters of SVM and LSSVM have no definite determination method.…”

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.

show abstract

A hybrid model based on synchronous optimisation for multi-step short-term wind speed forecasting

Cited by 131 publications

References 34 publications

Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

Multi-Objective Optimization of Start-up Strategy for Pumped Storage Units

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

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