Optimización del consumo eléctrico mediante la heurística cúmulo de partículas

• Blanca Nydia Perez-Camacho ^[1] ; Juan Manuel Gonzalez-Calleros ^[1] ; Gustavo Rodriguez-Gomez ^[2]
  1. [1] Benemérita Universidad Autónoma de Puebla

     Benemérita Universidad Autónoma de Puebla

     México

     
  2. [2] Instituto Nacional de Astrofísica, Óptica y Electrónica

     Instituto Nacional de Astrofísica, Óptica y Electrónica

     México

     
• Localización: Revista Colombiana de Computación, ISSN 1657-2831, ISSN-e 2539-2115, Vol. 22, Nº. 2, 2021, págs. 14-21
• Idioma: español
• DOI: 10.29375/25392115.4293
• Títulos paralelos:
  • Electrical consumption optimization through particle swarm optimization
• Enlaces
  • Texto completo (pdf)
• Resumen
  • español

    En el presente trabajo se da una breve explicación de la técnica de optimización por cúmulo de partículas para ser implementada como parte de la búsqueda del estado óptimo de consumo de un conjunto de dispositivos. Los dispositivos de uso doméstico, en conjunto, permiten caracterizar el consumo eléctrico de una casa habitación a través del comportamiento de uso. Cada uno de los dispositivos presenta un comportamiento de consumo. El objetivo de la optimización se refleja en la función objetivo, la cual es definida de acuerdo con el propósito general de implementación. Los datos de consumo de los dispositivos eléctricos son almacenados en vectores de consumo-hora, donde cada una de las posiciones corresponde al consumo generado por un dispositivo en una hora determinada. Cada uno de los vectores es usado por la heurística como un vector de referencia durante la búsqueda para encontrar el vector que cumple con la función objetivo.

  • English

    This paper gives a brief explanation of the particle swarm optimization technique, which is given to be implemented to look for the optimal state of consumption from a set of household appliances. The household appliances allow characterizing the electrical consumption of a dwelling house through use behavior. Every household appliance shows a behavior consumption. The goaloptimization objective is seen as the objective function defined according to the general implementation purpose. The consumption data of household appliances are stored in hourly consumption vectors, where everyone's position corresponds to the consumption generated by a household appliance in each hour. The heuristics use each of the vectors as a reference vector during the search to find the vector that fulfills the objective function.

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