scholarly journals Short-Term Wind Power Forecasting: A New Hybrid Model Combined Extreme-Point Symmetric Mode Decomposition, Extreme Learning Machine and Particle Swarm Optimization

2018 ◽  
Vol 10 (9) ◽  
pp. 3202 ◽  
Author(s):  
Jianguo Zhou ◽  
Xuechao Yu ◽  
Baoling Jin
Keyword(s):  
Particle Swarm Optimization ◽  
Wind Power ◽  
Extreme Point ◽  
The Other ◽  
Symmetric Mode ◽  
Short Term ◽  
Swarm Optimization ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Learning Machine

The nonlinear and non-stationary nature of wind power creates a difficult challenge for the stable operation of the power system when it accesses the grid. Improving the prediction accuracy of short-term wind power is beneficial to the power system dispatching department in formulating a power generation plan, reducing the rotation reserve capacity and improving the safety and reliability of the power grid operation. This paper has constructed a new hybrid model, named the ESMD-PSO-ELM model, which combines Extreme-point symmetric mode decomposition (ESMD), Extreme Learning Machine (ELM) and Particle swarm optimization (PSO). Firstly, the ESMD is applied to decompose wind power into several intrinsic mode functions (IMFs) and one residual(R). Then, the PSO-ELM is applied to predict each IMF and R. Finally, the predicted values of these components are assembled into the final forecast value compared with the original wind power. To verify the predictive performance of the proposed model, this paper selects actual wind power data from 1 April 2016 to 30 April 2016 with a total of 2880 observation values located in Yunnan, China for the experimental sample. The MAPE, NMAE and NRMSE values of the proposed model are 4.76, 2.23 and 2.70, respectively, and these values are lower than those of the other eight models. The empirical study demonstrates that the proposed model is more robust and accurate in forecasting short-term wind power compared with the other eight models.

scholarly journals Forecasting the Carbon Price Using Extreme-Point Symmetric Mode Decomposition and Extreme Learning Machine Optimized by the Grey Wolf Optimizer Algorithm

Energies ◽  
2019 ◽  
Vol 12 (5) ◽  
pp. 950 ◽  
Author(s):  
Jianguo Zhou ◽  
Xuejing Huo ◽  
Xiaolei Xu ◽  
Yushuo Li
Keyword(s):  
Extreme Point ◽  
Extreme Learning Machine ◽  
Carbon Price ◽  
Grey Wolf Optimizer ◽  
Symmetric Mode ◽  
Grey Wolf ◽  
Intrinsic Mode Functions ◽  
Mode Decomposition ◽  
Proposed Model ◽  
Learning Machine

Due to the nonlinear and non-stationary characteristics of the carbon price, it is difficult to predict the carbon price accurately. This paper proposes a new novel hybrid model for carbon price prediction. The proposed model consists of an extreme-point symmetric mode decomposition, an extreme learning machine, and a grey wolf optimizer algorithm. Firstly, the extreme-point symmetric mode decomposition is employed to decompose the carbon price into several intrinsic mode functions and one residue. Then, the partial autocorrelation function is utilized to determine the input variables of the intrinsic mode functions, and the residue of the extreme learning machine. In the end, the grey wolf optimizer algorithm is applied to optimize the extreme learning machine, to forecast the carbon price. To illustrate the superiority of the proposed model, the Hubei, Beijing, Shanghai, and Guangdong carbon price series are selected for the predictions. The empirical results confirm that the proposed model is superior to the other benchmark methods. Consequently, the proposed model can be employed as an effective method for carbon price series analysis and forecasting.

scholarly journals Models for Short-Term Wind Power Forecasting Based on Improved Artificial Neural Network Using Particle Swarm Optimization and Genetic Algorithms

Energies ◽  
2020 ◽  
Vol 13 (11) ◽  
pp. 2873 ◽  
Author(s):  
Dinh Thanh Viet ◽  
Vo Van Phuong ◽  
Minh Quan Duong ◽  
Quoc Tuan Tran
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Electricity Market ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Short Term ◽  
Swarm Optimization

As sources of conventional energy are alarmingly being depleted, leveraging renewable energy sources, especially wind power, has been increasingly important in the electricity market to meet growing global demands for energy. However, the uncertainty in weather factors can cause large errors in wind power forecasts, raising the cost of power reservation in the power system and significantly impacting ancillary services in the electricity market. In pursuance of a higher accuracy level in wind power forecasting, this paper proposes a double-optimization approach to developing a tool for forecasting wind power generation output in the short term, using two novel models that combine an artificial neural network with the particle swarm optimization algorithm and genetic algorithm. In these models, a first particle swarm optimization algorithm is used to adjust the neural network parameters to improve accuracy. Next, the genetic algorithm or another particle swarm optimization is applied to adjust the parameters of the first particle swarm optimization algorithm to enhance the accuracy of the forecasting results. The models were tested with actual data collected from the Tuy Phong wind power plant in Binh Thuan Province, Vietnam. The testing showed improved accuracy and that this model can be widely implemented at other wind farms.

An Adaptive Chaos Quantum-Behaved Particle Swarm Optimization Algorithm for Real-Time Dispatch in Power System with Wind Generation

2014 ◽  
Vol 1070-1072 ◽  
pp. 297-302
Author(s):  
Zhi Kui Wu ◽  
Chang Hong Deng ◽  
Yong Xiao ◽  
Wei Xing Zhao ◽  
Qiu Shi Xu
Keyword(s):  
Particle Swarm Optimization ◽  
Power System ◽  
Real Time ◽  
Wind Power ◽  
Wind Farm ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Swarm Optimization ◽  
Good Convergence ◽  
Proposed Model

A real-time dispatch (RTD) model for wind power incorporated power system aimed at maximizing wind power utilization and minimizing fuel cost is proposed in this paper. To cope with the prematurity and local convergence of conventional particle swarm optimization (PSO) algorithm, a novel adaptive chaos quantum-behaved particle swarm optimization (ACQPSO) algorithm is put forward. The adaptive inertia weight and chaotic perturbation mechanism are employed to improve the particle’s search efficiency. Numerical simulation on a 10 unit system with a wind farm demonstrates that the proposed model can maximize wind power utilization while ensuring the safe and economic operation of the power system. The proposed ACQPSO algorithm is of good convergence quality and the computation speed can meet the requirement of RTD.

Wavelet Neural Network Model for Short-Term Wind Power Forecasting Based on Particle Swarm Optimization Algorithm

2014 ◽  
Vol 543-547 ◽  
pp. 806-812 ◽  
Author(s):  
Ye Chen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Wind Power ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Wavelet Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important to the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network is proposed. At the same time in order to overcome the disadvantages of the wavelet neural network for only use error reverse transmission as a fixed rule, this paper puts forward using Particle Swarm Optimization algorithm to replace the traditional gradient descent method training wavelet neural network. Through the analysis of the measured data of a wind farm, Shows that the forecasting method can improve the accuracy of the wind power prediction, so it has great practical value.

ABC analysis using Particle Swarm Optimization and its performance evaluation with other models

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Kathirvel Selvaraju ◽  
Punniyamoorthy Murugesan
Keyword(s):  
Particle Swarm Optimization ◽  
Inventory Management ◽  
Performance Measures ◽  
Cost Minimization ◽  
Particle Swarm ◽  
The Other ◽  
Swarm Optimization ◽  
Content Type ◽  
Proposed Model ◽  
Inventory Turnover

PurposeThe purpose of this article is to develop a cost-effective model for Multi-Criteria ABC Inventory Classification and to measure its performance in comparison to the other existing models.Design/methodology/approachParticle Swarm Optimization (PSO) algorithm is exclusively designed for Multi-Criteria ABC Inventory Classification wherein the inventory is classified based on the objective of cost minimization, which is achieved through the inventory performance index – total relevant cost. Effectiveness of classification of the proposed model and the other classification models toward two inventory performance measures, that is, cost and inventory turnover has been computed, and the results of all models are relatively compared by arriving at the cumulative performance score of each model.FindingsThis study reveals that the ABC Inventory classification based on the proposed PSO approach is more effective toward cost and inventory turnover ratio in comparison to the twenty existing models.Practical implicationsThe proposed model can be easily adapted to the industrial requirement of inventory classification by cost as objective as well as other inventory management performance measures.Originality/valueThe conceptual model is more versatile which can be adapted for various objectives and the effectiveness of classification in comparison to the other models can be measured toward each objective as well as combining all the objectives.

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