Parameter estimation of photovoltaic system using imperialist competitive algorithm

Fathy, Ahmed; Rezk, Hegazy

doi:10.1016/j.renene.2017.04.014

Cited by 158 publications

(58 citation statements)

References 26 publications

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“…Therefore, it was concluded that compared with the traditional BP neural network model, the real estate evaluation model based on genetic algorithm optimized neural network had more remarkable performance, the prediction error was smaller, and the fitting degree with the experimental data was higher. The reason for the result was that the genetic neural network selected the most appropriate weight and threshold, the selection effects of the weight and threshold were better than methods proposed in Ying et al (2017) and Chen et al (2015); the optimal BP neural network was obtained, which improved the prediction performance, and the prediction performance was superior to the imperialist competitive algorithm (Fathy and Rezk, 2017). The work proved the feasibility and practicability of the proposed method and provides a new way for the evaluation of real estate.…”

Section: Resultsmentioning

confidence: 88%

Real Estate Evaluation Model Based on Genetic Algorithm Optimized Neural Network

Sun¹

2019

Data Science Journal

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With the rapid development of society, the real estate economy, as an important part of Chinese economy, is showing a growing trend. But it is also the most likely to generate bubble economy, causing financial risks; it will trigger a series of social contradictions and cause social unrest in severe cases. Therefore, it is urgent to improve and optimize the real estate evaluation model. In this study, the real estate was evaluated based on the neural network model optimized by genetic algorithm. Through sorting out and summarizing the real estate data in a period of time, the corresponding model was established and the test data were obtained. The average relative error value of the genetic algorithm optimized neural network model was 3.552, which was smaller than that of the Back-Propagation (BP) neural network prediction model. The experimental conclusion that the new network model was better than the traditional model was obtained. This work opens up a new route of real estate evaluation.It was found from Figure 4 that with the increase of training times, the mean square error of the two models decreased, and the overall trend was downward. With the increase of training times, the accuracy was improved. However, under the same number of training, the mean square error of the optimal BP neural network obtained by the genetic neural network model was smaller than that obtained by the BP neural network model. Therefore, the accuracy of genetic neural network in real estate data evaluation was much higher than that of BP neural network. Moreover the convergence speed of the training of the genetic neural network model was much faster than that of the BP neural network model. The reason was that the genetic neural network model had screened out the most appropriate weights and thresholds, and the optimal BP neural network was obtained, which greatly improved the training convergence speed.It was found from Figure 5 that there was a large difference between the real price and the price evaluated by BP neural network model, the trend of them was different, and the overall fitting degree was low. The difference between the real estate price and the price evaluated by the genetic neural network was small, the trend of them was basically similar, and the overall fitting degree was high. Therefore, it was concluded that the genetic neural network model was more accurate for real estate evaluation data and had a higher overall fitting degree.

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Section: Resultsmentioning

confidence: 88%

Real Estate Evaluation Model Based on Genetic Algorithm Optimized Neural Network

Sun¹

2019

Data Science Journal

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“…The estimated parameters obtained with the fiveparameter procedure are also compared with the ones previously reported by other studies [8][9][10][11][12]. Ref.…”

Section: Resultsmentioning

confidence: 90%

“…Ref. [8] use a methodology based on imperialist comparative algorithm. To obtain the five parameters, Ref.…”

Section: Resultsmentioning

confidence: 99%

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Simple vs complex models for solar cells

Sabadus

Paulescu

2019

E3S Web Conf.

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In this paper three different procedures for extracting the current-voltage characteristics of a crystalline photovoltaic module are studied. Each procedure is associated to a solar cell model characterized by a well-defined degree of complexity. The results emphasize that the simple models approximate the current-voltage characteristics of a solar cell as good as the more complex models. Even if all the procedures analysed in this paper approximate well the measured characteristics, the specific model parameters experience a large dispersion. From a broader perspective, the results raise a question mark on the ability of the current procedures to accurately extract the solar cell parameters.

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“…[20] ICA is a metaheuristic based on social and political behavior [21]. At present, the algorithm has been successfully applied to power grid energy management [22], nuclear reactor fuel loading [23], parameter estimation [24], production scheduling [25][26][27][28][29], and so on. According to literature [30], ICA not only is an effective global optimization method, but also has a strong neighborhood search capability.…”

Section: Introductionmentioning

confidence: 99%

A Novel Imperialist Competitive Algorithm for Multithreshold Image Segmentation

Wang¹,

Guo-hua

Liu³

2019

Mathematical Problems in Engineering

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Multithreshold image segmentation plays a very important role in computer vision and pattern recognition. However, the computational complexity of multithreshold image segmentation increases exponentially with the increasing number of thresholds. Thus, in this paper, a novel imperialist competitive algorithm is proposed to solve the multithreshold image segmentation problem. Firstly, a new strategy of revolution and assimilation is adopted to improve the search efficiency of the algorithm. Secondly, imperialist self-learning and reserve country set are introduced to enhance the search of outstanding individuals in the population. Combining with the reserve country set, a novel imperialist competition strategy is proposed to remove the poorer individuals and improve the overall quality of the population. Finally, the sensitivity of the algorithm parameters is analyzed. Ten standard test pictures are selected to test. The experimental results show that the novel imperialist competitive algorithm has faster convergence speed, higher quality, and higher stability in solving multithreshold segmentation problems than methods from literature.

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Parameter estimation of photovoltaic system using imperialist competitive algorithm

Cited by 158 publications

References 26 publications

Real Estate Evaluation Model Based on Genetic Algorithm Optimized Neural Network

Real Estate Evaluation Model Based on Genetic Algorithm Optimized Neural Network

Simple vs complex models for solar cells

A Novel Imperialist Competitive Algorithm for Multithreshold Image Segmentation

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