A switching delayed PSO optimized extreme learning machine for short-term load forecasting

Zeng, Nianyin; Zhang, Hong; Liu, Weibo; Liang, Jinling; Alsaadi, Fuad E.

doi:10.1016/j.neucom.2017.01.090

Cited by 175 publications

(73 citation statements)

References 30 publications

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“…The proposed method developed an evolved-cooperation strategy to generate the optimized solution of the influence coefficients and the bandwidths which suits the distribution of the prediction errors. To achieve an accurate estimation, the bandwidth was generated based on switching delayed particle swarm optimization (SDPSO) [68] and the influence coefficients were calculated based on the cost function for estimating the probability distribution function of errors. The basic procedures of the method are as follows.…”

Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

“…The processes stop when the cost function of the model is stable. bandwidth was generated based on switching delayed particle swarm optimization (SDPSO) [68] and the influence coefficients were calculated based on the cost function for estimating the probability distribution function of errors. The basic procedures of the method are as follows.…”

Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

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Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Mei

Liu

et al. 2019

Entropy

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In recent years, the correntropy instead of the mean squared error has been widely taken as a powerful tool for enhancing the robustness against noise and outliers by forming the local similarity measurements. However, most correntropy-based models either have too simple descriptions of the correntropy or require too many parameters to adjust in advance, which is likely to cause poor performance since the correntropy fails to reflect the probability distributions of the signals. Therefore, in this paper, a novel correntropy-based extreme learning machine (ELM) called ECC-ELM has been proposed to provide a more robust training strategy based on the newly developed multi-kernel correntropy with the parameters that are generated using cooperative evolution. To achieve an accurate description of the correntropy, the method adopts a cooperative evolution which optimizes the bandwidths by switching delayed particle swarm optimization (SDPSO) and generates the corresponding influence coefficients that minimizes the minimum integrated error (MIE) to adaptively provide the best solution. The simulated experiments and real-world applications show that cooperative evolution can achieve the optimal solution which provides an accurate description on the probability distribution of the current error in the model. Therefore, the multi-kernel correntropy that is built with the optimal solution results in more robustness against the noise and outliers when training the model, which increases the accuracy of the predictions compared with other methods.

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Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

Section: The Framework Of the Proposed Methodsmentioning

confidence: 99%

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Mei

Liu

et al. 2019

Entropy

View full text Add to dashboard Cite

show abstract

“…For such a high-dimensional search space, the number of particles n is selected as 200. According to [23], we set acceleration factor c 1 equal to 0.5, c 2 equal to 1.25, and inertia weight w equal to 0.9.…”

Section: Parameter Setting Of Psomentioning

confidence: 99%

Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN‐PSO

et al. 2018

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A novel hybrid algorithm that employs BP neural network (BPNN) and particle swarm optimization (PSO) algorithm is proposed for the kinematic parameter identification of industrial robots with an enhanced convergence response. The error model of the industrial robot is established based on a modified Denavit-Hartenberg method and Jacobian matrix. Then, the kinematic parameter identification of the industrial robot is transformed to a nonlinear optimization in which the unknown kinematic parameters are taken as optimal variables. A hybrid algorithm based on a BPNN and the PSO is applied to search for the optimal variables which are used to compensate for the error of the kinematic parameters and improve the positioning accuracy of the industrial robot. Simulations and experiments based on a realistic industrial robot are all provided to validate the efficacy of the proposed hybrid identification algorithm. The results show that the proposed parameter-identification method based on the BPNN and PSO has fewer iterations and faster convergence speed than the standard PSO algorithm.

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“…Hence, the massive computation and time cost of quantile methods are unbearable. Although interval methods with the application of ELM can overcome these limitations by removing training steps, the accuracy of output highly depends on its random given parameters [32]. In addition, the fixed intervals of PI methods and the redundant uncertainty of CI methods are adverse.…”

Section: Introductionmentioning

confidence: 99%

Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

et al. 2019

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Precise prediction of short-term electric load demand is the key for developing power market strategies. Due to the dynamic environment of short-term load forecasting, probabilistic forecasting has become the center of attention for its ability of representing uncertainty. In this paper, an integration scheme mainly composed of correlation analysis and improved weighted extreme learning machine is proposed for probabilistic load forecasting. In this scheme, a novel cooperation of wavelet packet transform and correlation analysis is developed to deal with the data noise. Meanwhile, an improved weighted extreme learning machine with a new switch algorithm is provided to effectively obtain stable forecasting results. The probabilistic forecasting task is then accomplished by generating the confidence intervals with the Gaussian process. The proposed integration scheme, tested by actual data from Global Energy Forecasting Competition, is proved to have a better performance in graphic and numerical results than the other available methods.

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A switching delayed PSO optimized extreme learning machine for short-term load forecasting

Cited by 175 publications

References 30 publications

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Evolved-Cooperative Correntropy-Based Extreme Learning Machine for Robust Prediction

Hybrid Optimal Kinematic Parameter Identification for an Industrial Robot Based on BPNN‐PSO

Probabilistic Forecasting of Short-Term Electric Load Demand: An Integration Scheme Based on Correlation Analysis and Improved Weighted Extreme Learning Machine

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