A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm

Wang, Jianzhou; Yang, Wendong; Du, Pei; Niu, Tong

doi:10.1016/j.enconman.2018.02.012

Cited by 197 publications

(56 citation statements)

References 55 publications

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“…Sine cosine algorithm (SCA) is a new swarm intelligence method that was proposed recently by Mirjalili [12]. Since its introduction, SCA has successfully found its applications for many practical problems [13][14][15][16][17][18]. However, like other intelligent algorithms [19][20][21][22][23][24][25], the original SCA is easy to fall into the local optimum when solving the practical problems.…”

Section: Introductionmentioning

confidence: 99%

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Lin

Chen

et al. 2019

Mathematical Problems in Engineering

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Under the background of "innovation and entrepreneurship," how to scientifically and rationally choose employment or independent entrepreneurship according to their own comprehensive situation is of great significance to the planning and development of their own career and the social adaptation of university personnel training. This study aims to develop an adaptive support vector machine framework, called RF-CSCA-SVM, for predicting college students' entrepreneurial intention in advance; that is, students choose to start a business or find a job after graduation. RF-CSCA-SVM combines random forest (RF), support vector machine (SVM), sine cosine algorithm (SCA), and chaotic local search. In this framework, RF is used to select the most important factors; SVM is employed to establish the relationship model between the factors and the students' decision to choose to start their own business or look for jobs. SCA is used to tune the optimal parameters for SVM. Additionally, chaotic local search is utilized to enhance the search capability of SCA. A total of 300 students were collected to develop the predictive model. To validate the developed method, other four meta-heuristic based SVM methods were used for comparison in terms of classification accuracy, Matthews Correlation Coefficients (MCC), sensitivity, and specificity. The experimental results demonstrate that the proposed method can be regarded as a promising success with the excellent predictive performance. Promisingly, the established adaptive SVM framework might serve as a new candidate of powerful tools for entrepreneurial intention prediction.

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Section: Introductionmentioning

confidence: 99%

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Lin

Chen

et al. 2019

Mathematical Problems in Engineering

View full text Add to dashboard Cite

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“…The sine cosine algorithm (SCA) is a brand new evolutionary algorithm inspired by the sine and cosine functions [34,35]. The key point of SCA is to iteratively explore unknown problem space through a simple but powerful mathematical structure and model [36,37].…”

Section: Introductionmentioning

confidence: 99%

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

et al. 2019

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In recent years, growing peak pressure is posing a huge challenge for the operators of electrical power systems. As the most important clean renewable energy, hydropower is often advised as a response to the peak loads in China. Thus, a novel hybrid sine cosine algorithm (HSCA) is proposed to deal with the complex peak operation problem of cascade hydropower reservoirs. In HSCA, the elite-guide evolution strategy is embedded into the standard sine cosine algorithm to improve the convergence rate of the swarm. The Gaussian local search strategy is used to increase the diversity of the population. The random mutation operator is adopted to enhance the search capability of the individuals in the evolutionary process. The proposed method is applied to solve the complex peak operation problem of two hydropower systems. The simulations indicate that in different cases, HSCA can generate the scheduling results with higher quality than several benchmark methods. Hence, this paper provides a feasible method for the complex peak operation problem of cascade hydropower reservoirs.

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“…More specifically, it is reasonable to put hybrid forecasting models into two categories. The first category is usually based on an individual forecasting method with the addition of a data preprocessing strategy or an intelligent optimization algorithm or both, forming a model with a multi-layer structure [15]. Examples of the application of such models in load forecasting are given below.…”

Section: Introductionmentioning

confidence: 99%

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

Wang

Tang

et al. 2019

Energies

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Electricity load forecasting plays an essential role in improving the management efficiency of power generation systems. A large number of load forecasting models aiming at promoting the forecasting effectiveness have been put forward in the past. However, many traditional models have no consideration for the significance of data preprocessing and the constraints of individual forecasting models. Moreover, most of them only focus on the forecasting accuracy but ignore the forecasting stability, resulting in nonoptimal performance in practical applications. This paper presents a novel hybrid model that combines an advanced data preprocessing strategy, a deep neural network, and an avant-garde multi-objective optimization algorithm, overcoming the defects of traditional models and thus improving the forecasting performance effectively. In order to evaluate the validity of the proposed hybrid model, the electricity load data sampled in 30-min intervals from Queensland, Australia are used as a case to study. The experiments show that the new proposed model is obviously superior to all other traditional models. Furthermore, it provides an effective technical forecasting means for smart grid management.

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A novel hybrid forecasting system of wind speed based on a newly developed multi-objective sine cosine algorithm

Cited by 197 publications

References 55 publications

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Predict the Entrepreneurial Intention of Fresh Graduate Students Based on an Adaptive Support Vector Machine Framework

Peak Operation Problem Solving for Hydropower Reservoirs by Elite-Guide Sine Cosine Algorithm with Gaussian Local Search and Random Mutation

Research and Application of a Novel Hybrid Model Based on a Deep Neural Network for Electricity Load Forecasting: A Case Study in Australia

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