IEEE Power & Energy Magazine - Spanish - May/June 2022 - 35

Datos de entrada
del modelo
pd−1
pd−2
...
pd−7
Xd
...
D
(a)
u2
...
un
pd,2
...
pd,24
...
D
(b)
figura 5. Las arquitecturas de RA de múltiples datos de salida para la EPF: (a) superficiales y (b) profundas con dos capas
ocultas. La notación es similar a la de las figuras 1 y 3. Las flechas punteadas representan las conexiones de retroalimentación;
si existen, la estructura se llama RN recurrente. (Fuente: el gráfico de la evolución humana es una modificación del
" Human Evolution Scheme " (Esquema de evolución humana) de José-Manuel Benitos. Disponible bajo los términos de la
licencia de documentación libre de GNU).
En términos del entrenamiento y el uso en tiempo real, la
RNP consta de dos fases. En el primero, los hiperparámetros
y las características de los datos de entrada se optimizan en
conjunto usando datos históricos. Para esto, se modelan los
datos de entrada como hiperparámetros binarios, que pueden
ser seleccionados o descartados. Este paso se realiza
de manera periódica, pero no frecuente, es decir, una vez
al mes. En la segunda fase, la RNP se recalibra a diario con
el conjunto óptimo de datos de entrada y los hiperparámetros
para explicar los datos más nuevos del mercado. Debido
a que cada selección de hiperparámetros o características
arroja un óptimo local distinto, la RNP puede beneficiarse
mucho de las predicciones promedio, es decir, entrenar a
múltiples RNP y crear una predicción que sea el promedio
aritmético de las predicciones individuales de las RNP.
La primera ola de modelos de AP vino acompañada, en
los últimos años, de arquitecturas híbridas que usan las llamadas
RN convolucionales o de memoria de corto y largo
plazo. Distinto de las redes de prealimentación, la arquitectura
de memoria de corto y largo plazo tiene múltiples conexiones
de retroalimentación y puede procesar no solo puntos
de datos individuales, sino también series completas de
tiempo. Las RN convolucionales son versiones regularizadas,
para evitar el sobreajuste, de redes de prealimentación
con múltiples capas que usan convolución (en lugar de una
multiplicación de matrices) en al menos una de sus capas.
mayo/junio 2022
No obstante, a pesar de los trabajos recientes sobre
modelos de AP, no es claro si toda la complejidad extra
conlleva mejoras en la precisión de las predicciones. La
MAE relativo
PJM
LEAR
DNN
0.7
0.6
Nord
Pool
EPEX
0.5
0.4
Alemania
u1
pd,1
Capa oculta
Datos de
salida del modelo
Datos de entrada
del modelo
pd−1
pd−2
...
pd−7
Xd
u1,n
u2,m
pd,24
u1,2
...
u2,2
...
pd,2
...
u1,1
u2,1
pd,1
Capa oculta 1Capa oculta 2Datos de
salida del modelo
EPEX
Francia
EPEX
Bélgica
figura 6. Una representación gráfica del radar de los
errores absolutos medios relativos (rMAE, por sus siglas en

IEEE Power & Energy Magazine - Spanish - May/June 2022

Table of Contents for the Digital Edition of IEEE Power & Energy Magazine - Spanish - May/June 2022

Contents
IEEE Power & Energy Magazine - Spanish - May/June 2022 - Cover1
IEEE Power & Energy Magazine - Spanish - May/June 2022 - Cover2
IEEE Power & Energy Magazine - Spanish - May/June 2022 - Contents
IEEE Power & Energy Magazine - Spanish - May/June 2022 - 2
IEEE Power & Energy Magazine - Spanish - May/June 2022 - 3
IEEE Power & Energy Magazine - Spanish - May/June 2022 - 4
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IEEE Power & Energy Magazine - Spanish - May/June 2022 - Cover3
IEEE Power & Energy Magazine - Spanish - May/June 2022 - Cover4
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