A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem

Yang, Zhile; Li, Kang; Niu, Qun; Xue, Yusheng

doi:10.1016/j.knosys.2017.07.013

Cited by 40 publications

(21 citation statements)

References 75 publications

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“…The improper tuning of algorithm-specific parameters either increases the computational effort or yields the local optimal solution. In our early work [97][98][99], teaching-learning-based optimization (TLBO) has been utilized for training an RBF neural network battery model. The TLBO method does not have any algorithmspecific parameters and significantly reduces the load of tuning work.…”

Section: Statistical Processing Controlmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

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Efficient and valuable strategies provided by large amount of available data are urgently needed for a sustainable electricity system that includes smart grid technologies and very complex power system situations. Big Data technologies including Big Data management and utilization based on increasingly collected data from every component of the power grid are crucial for the successful deployment and monitoring of the system. This paper reviews the key technologies of Big Data management and intelligent machine learning methods for complex power systems. Based on a comprehensive study of power system and Big Data, several challenges are summarized to unlock the potential of Big Data technology in the application of smart grid. This paper proposed a modified and optimized structure of the Big Data processing platform according to the power data sources and different structures. Numerous open-sourced Big Data analytical tools and software are integrated as modules of the analytic engine, and self-developed advanced algorithms are also designed. The proposed framework comprises a data interface, a Big Data management, analytic engine as well as the applications, and display module. To fully investigate the proposed structure, three major applications are introduced: development of power grid topology and parallel computing using CIM files, high-efficiency load-shedding calculation, and power system transmission line tripping analysis using 3D visualization. The real-system cases demonstrate the effectiveness and great potential of the Big Data platform; therefore, data resources can achieve their full potential value for strategies and decision-making for smart grid. The proposed platform can provide a technical solution to the multidisciplinary cooperation of Big Data technology and smart grid monitoring.

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Section: Statistical Processing Controlmentioning

confidence: 99%

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

et al. 2018

Self Cite

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“…To verify the prediction performance of the hybrid EEMD-HGSA-MKLSSVM model, three statical indices, namely RMSE, MAE, and MAPE, are utilized to measure the prediction accuracy, and these indices are expressed as (23), (24), and (25)…”

Section: Forecasting Performance Evaluation Indicesmentioning

confidence: 99%

“…Even though these signal decomposition based models have obtained good forecasting results, Wang et al [24] pointed out that not all decomposed subseries are a benefit for the final wind speed forecasting. To address this problem, the feature selection method is utilized widely [25,26]. In [8], Kullback-Leibler divergence-based and energy-based feature selections were exploited to identify the illusive components caused by the decomposed method EEMD.…”

Section: Introductionmentioning

confidence: 99%

A Compound Structure for Wind Speed Forecasting Using MKLSSVM with Feature Selection and Parameter Optimization

Sun

Zhu

et al. 2018

Mathematical Problems in Engineering

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The aims of this study contribute to a new hybrid model by combining ensemble empirical mode decomposition (EEMD) with multikernel function least square support vector machine (MKLSSVM) optimized by hybrid gravitation search algorithm (HGSA) for short-term wind speed prediction. In the forecasting process, EEMD is adopted to make the original wind speed data decomposed into intrinsic mode functions (IMFs) and one residual firstly. Then, partial autocorrelation function (PACF) is applied to identify the correlation between the corresponding decomposed components. Subsequently, the MKLSSVM using multikernel function of radial basis function (RBF) and polynomial (Poly) kernel function by weight coefficient is exploited as core forecasting engine to make the short-term wind speed prediction. To improve the regression performance, the binary-value GSA (BGSA) in HGSA is utilized as feature selection approach to remove the ineffective candidates and reconstruct the most relevant feature input-matrix for the forecasting engine, while real-value GSA (RGSA) makes the parameter combination optimization of MKLSSVM model. In the end, these respective decomposed subseries forecasting results are combined into the final forecasting values by aggregate calculation. Numerical results and comparable analysis illustrate the excellent performance of the EEMD-HGSA-MKLSSVM model when applied in the short-term wind speed forecasting.

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“…However, meta-heuristics generally have the problems of slow convergence and poor solution quality, and many researchers have taken many useful attempts in parallelization [36][37][38][39]. Schutte et al [40] proposes the coarse-grained parallelization of PSO to solve the problems of large-scale data, low cost and multiple local optimal solutions.…”

Section: Introductionmentioning

confidence: 99%

Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power

Pan

Chu

2019

Processes

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Wind and other renewable energy protects the ecological environment and improves economic efficiency. However, it is difficult to accurately predict wind power because of the randomness and volatility of wind. This paper proposes a new parallel heterogeneous model to predict the wind power. Parallel meta-heuristic saves computation time and improves solution quality. Four communication strategies, which include ranking, combination, dynamic change and hybrid, are introduced to balance exploration and exploitation. The dynamic change strategy is to dynamically increase or decrease the members of subgroup to keep the diversity of the population. The benchmark functions show that the algorithms have excellent performance in exploration and exploitation. In the end, they are applied to successfully realize the prediction for wind power by training the parameters of the neural network.

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A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem

Abstract: This is a repository copy of A novel parallel-series hybrid meta-heuristic method for solving a hybrid unit commitment problem.

Cited by 40 publications

References 75 publications

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

Complex Power System Status Monitoring and Evaluation Using Big Data Platform and Machine Learning Algorithms: A Review and a Case Study

A Compound Structure for Wind Speed Forecasting Using MKLSSVM with Feature Selection and Parameter Optimization

Novel Parallel Heterogeneous Meta-Heuristic and Its Communication Strategies for the Prediction of Wind Power

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