Implementation of ANFIS +NN and nature-inspired optimization algorithms for solar radiation prediction
DOI:
https://doi.org/10.22517/23447214.25682Palabras clave:
ANFIS, Redes Neuronales, Algoritmos de optimizacion, Prediccion de radiacion solar, Sistemas FuzzyResumen
Este artículo presenta un modelo híbrido que utiliza ANFIS (sistema de inferencia neurodifusa adaptativa) hibridado con redes neuronales (NN) y optimizado con algoritmos basados en comportamientos naturales, en este caso, colonias de hormigas (ACO). El modelo está diseñado para predecir los recursos primarios en una región específica, sujeta a la planificación e instalación de generación fotovoltaica (FV) distribuida. El recurso solar primario depende de las condiciones climáticas de la región a evaluar, y su alta variabilidad en períodos cortos representa un desafío en la planificación de recursos energéticos que utilizan fuentes variables a largo plazo. En este artículo, se diseña, desarrolla, evalúa y valida el comportamiento de un modelo híbrido ANFIS+NN+ACO. Se detalla la metodología basada en el análisis de datos. En primer lugar, se trabaja con bases de datos climáticas, que proporcionan pautas para el preprocesamiento y la limpieza. En segundo lugar, se establecen las variables climáticas que predicen la radiación solar. Las funciones de pertenencia de ANFIS se basan en los datos para capturar la no linealidad y extraer relaciones con los predictores. Las redes neuronales respaldan el proceso de optimización de la función de pertenencia y, finalmente, el optimizador refina y evalúa la respuesta. Esta se evalúa mediante métricas que demuestran la robustez del modelo al capturar y procesar datos. El estudio contribuye a visibilizar herramientas y alternativas para determinar el potencial energético en regiones climáticas sujetas al futuro de la generación distribuida.
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