Forecast of Distributed Electrical Generation System Capacity Based on Seasonal Micro Generators using ELM and PSO

Rocha, Helder R. O.; Silvestre, Leonardo J.; Celeste, Wanderley Cardoso; Coura, Daniel José Custódio; Rigo, Luis Otavio

doi:10.1109/tla.2018.8362148

Cited by 20 publications

(7 citation statements)

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“…In recent years, LA countries have contributed novel proposals in Demand Side Management, forecasting models, and theoretical studies for forecasting optimization (Cruz, Alvarez, Al-Sumaiti, & Rivera, 2020;Cruz, Alvarez, Rivera, & Herrera, 2019;Diaz, Vuelvas, Ruiz, & Patino, 2019;Garcia-Guarin et al, 2019;J. Garcia, Alvarez, & Rivera, 2020; J. R. Garcia, Zambrano P, & Duarte, 2018;Henríquez & Kristjanpoller, 2019;Hernandez & Baeza, 2019;Jiménez, Pertuz, Quintero, & Montaña, 2019;Marrero, García-Santander, Carrizo, & Ulloa, 2019;Moret, Babonneau, Bierlaire, & Maréchal, 2020;Paredes, Vargas, & Maldonado, 2020;Ramirez, Cruz, & Gutierrez, 2019;Rocha, Silvestre, Celeste, Coura, & Rigo, 2018;Romero-Quete & Canizares, 2019;Sanhueza & Freitas, 2018;Zavadzki, Kleina, Drozda, & Marques, 2020;Zuniga-Garcia, Santamaría-Bonfil, Arroyo-Figueroa, & Batres, 2019).…”

Section: Smart Buildings Forecasting Techniquesmentioning

confidence: 99%

Mejoras al desempeño energético en edificaciones abordando los desafíos actuales del demand-side: Una revisión de contribuciones de Latinoamérica

2020

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Debido a la actual crisis energética mundial y como resultado de los acuerdos de las naciones participantes, se han establecido medidas por las Naciones Unidas para superar los desafíos relacionados. A pesar de los esfuerzos para incluir a los países subdesarrollados en dicho proceso de decisión, la mayoría de las contribuciones continúan estando inclinadas al hemisferio norte. Así, este trabajo se enfoca en destacar los esfuerzos realizados por los países Latinoamericanos (LA), entre 2018-2020, para contribuir específicamente en las mejoras en el desempeño energético en edificaciones para abordar los desafíos actuales del lado de la demanda. Dichos desafíos están relacionados con la gestión de la demanda: (i) picos de demanda no controlados y (ii) capacidad de transmisión y distribución insuficiente en la red eléctrica. Las contribuciones de LA se clasifican en independientes, colaboración y aplicación. Los estudios también se clasificaron en teóricos, experimentales, ambos y revisiones. La metodología de filtrado de dos etapas implementada dio como resultado un total de 176 documentos como lista inicial. Al centrarse sólo en los aspectos relacionados con los ocupantes, las soluciones pasivas y de bajo consumo y las técnicas de previsión para edificios inteligentes, la lista procesada dio como resultado un total de 73 estudios. Los resultados mostraron que los esfuerzos realizados por los países LA residen en su mayoría en la implementación de estrategias previamente desarrolladas y propuestas por países desarrollados, para realizar estudios de caso como independiente o en colaboración. Finalmente, se presenta un análisis FODA para analizar más los resultados.

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Section: Smart Buildings Forecasting Techniquesmentioning

confidence: 99%

Mejoras al desempeño energético en edificaciones abordando los desafíos actuales del demand-side: Una revisión de contribuciones de Latinoamérica

2020

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“…The particle swarm optimization (PSO) algorithm is a natural imitation algorithm based on social behavior patterns of biological communities rather than evolutionary mechanism of natural selection, as proposed by Kennedy and Eberhart [39][40][41][42][43][44][45][46][47][48][49][50]. PSO is an algorithm for finding the optimal solution by mimicking the behavior habits of animals such as birds, fish, bees, and ants.…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…Step 3] Update the next position vector X i k and the velocity vector V i k of each object using the following formula [43,44].…”

Section: Particle Swarm Optimization (Pso)mentioning

confidence: 99%

“…[Step 2] In Step 1, we set , which is the minimum cost function, to the global optimum position vector of the whole group. [Step 3] Update the next position vector and the velocity vector of each object using the following formula [43,44] The structure of the incremental granular model based on the particle swarm optimization, which is the natural mimic optimization algorithm described above, can be expressed as shown in Figure 6, and the procedure is as follows.…”

Section: Particle Swarm Optimization-based Incremental Granular Modelmentioning

confidence: 99%

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Incremental Granular Model Improvement Using Particle Swarm Optimization

Yeom

Kwak

2019

Symmetry

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This paper proposes an incremental granular model (IGM) based on particle swarm optimization (PSO) algorithm. An IGM is a combination of linear regression (LR) and granular model (GM) where the global part calculates the error using LR. However, traditional CFCM clustering presents some problems because the number of clusters generated in each context is the same and a fixed value is used for fuzzification coefficient. In order to solve these problems, we optimize the number of clusters and their fuzzy numbers according to the characteristics of the data, and use natural imitative optimization PSO algorithm. We further evaluate the performance of the proposed method and the existing IGM by comparing the predicted performance using the Boston housing dataset. The Boston housing dataset contains housing price information in Boston, USA, and features 13 input variables and 1 output variable. As a result of the prediction, we can confirm that the proposed PSO-IGM shows better performance than the existing IGM.

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“…Therefore, Abdoos [50] applied the Gram-Schmidt orthogonalization (GSO), to optimize the ELM in order to forecast the wind power of two wind farms. Rocha et al [51] proposed the PSO to optimize the ELM to predict the distributed electrical generation system capacity. Sun et al [52] focused on the PSO-ELM to predict carbon price, and the results indicated that PSO-ELM performed better.…”

Section: Introductionmentioning

confidence: 99%

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

Zhou

Huo

et al. 2019

Energies

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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.

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Forecast of Distributed Electrical Generation System Capacity Based on Seasonal Micro Generators using ELM and PSO

Cited by 20 publications

References 0 publications

Mejoras al desempeño energético en edificaciones abordando los desafíos actuales del demand-side: Una revisión de contribuciones de Latinoamérica

Mejoras al desempeño energético en edificaciones abordando los desafíos actuales del demand-side: Una revisión de contribuciones de Latinoamérica

Incremental Granular Model Improvement Using Particle Swarm Optimization

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

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