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Unidades recurrentes cerradas (GRU) vs redes neuronales artificiales en la predicció= n de la generación eléctrica de la Central Hidroeléctrica Illuchi=

Closed recurring units (= GRU) vs artificial neural networks in the prediction of electricity generation at the Illuchi Hydroelectric Power Plant

¹<= /b>	Fernando Santiago Bustamante Freire=	https://orcid.org/000= 9-0004-2404-2544
	<= span style=3D'mso-bookmark:_Hlk139186595'>Universidad Técnica de Cotopaxi (UTC), Ecuador <= span style=3D'mso-bookmark:_Hlk139186595'>fernando.bustamante@utc.edu.ec
²<= /b>	Jessica Nataly Castillo Fiallos	https://orcid.org/000= 0-0002-3120-7229
	<= span style=3D'mso-bookmark:_Hlk139186595'>Universidad Técnica de Cotopaxi (UTC), Ecuador <= span style=3D'mso-bookmark:_Hlk139186595'>jessica.castillo@utc.edu.e= c
³<= /b>	Carlos Iván Quinatoa Caiza	https://orcid.org/000= 0-0001-6369-7480
	<= span style=3D'mso-bookmark:_Hlk139186595'>Universidad Técnica de Cotopaxi (UTC), Ecuador <= span style=3D'mso-bookmark:_Hlk139186595'>carlos.quinatoa7864@utc.edu.ec
⁴<= /b>	Héctor Raúl Reinoso Peńaherrera<= o:p>	https://orcid.org/000= 9-0008-4881-3920
	<= span style=3D'mso-bookmark:_Hlk139186595'>Universidad Técnica de Cotopaxi (UTC), Ecuador hector.reinoso@utc.edu.ec<= /span>

=	Artículo de Investigación Científica y Tecnológica Enviado: 12/05/2023 Revisado: 28/06/2023 Aceptado: 28/07/2023 Publicado:30/08/2023 DOI: <= /span>https://= doi.org/10.33262/ap.v5i3.395 <= span style=3D'mso-spacerun:yes'> = &nb= sp;

&nb= sp; Cítese:<= /span> <= o:p>	&nb= sp;	Bustamante Freire, F. S., Castillo Fiallos, J. N., Quinatoa Caiza, C. I., & Reinoso Peńaherrera, H. R. (2023). Unidades recurrentes cerradas (GRU) vs redes neuronales artificiales en la predicción de la generación eléctrica de la Central Hidroeléctrica Illuchi. = AlfaPublicaciones, 5(3), 150–166. https://doi.org/10.33262/ap.v5i3.395=
=	<= /p>	*ALFA PUBLICACIONES,* <= /span>es una Revista Multidisciplinar, Trimestr= al, que se publicará en soporte electrónico tiene como misión contribu= ir a la formación de profesionales competentes con visión humaníst= ica y crítica que sean capaces de exponer sus resultados investigativos y científicos en la misma medida que se promueva mediante su intervención cambios positivos en la sociedad. https://alfapublicaciones.com <= span style=3D'mso-bookmark:_Hlk139186595'>La revista es editada por la Editorial Cien= cia Digital (Editorial de prestigio registrada en la Cámara Ecuatoriana de Li= bro con No de Afiliación 663) www.celibro.org.ec= &= nbsp;
=	<= /p>	Esta revista está protegida b= ajo una licencia Creative Co= mmons AttributionNonCommercialNoDerivatives 4.0 International. Copia de la licencia: http://creativecommons.org/li= censes/by-nc-nd/4.0/
Palabras claves: ADAM, P= ython, GRU, predicción, redes neuronales artificiales. = <= /span>		Resumen La predicción = de eventos ha sido desde la antigüedad, un fenómeno capaz de generar curiosi= dad en el ser humano, sin embargo, para lograr una proyección de un evento fu= turo se requiere de un análisis detallado de datos para predecir eventos posteriores, con esta idea. Objetivos: el objetivo de la investigación fue desarrollar dos sistemas de predicción aplicando redes neuronales artificiales y GRU para determinar la generación eléctrica pronosticada e= n la CENTRAL HIDROELÉCTRICA ILLUCHI. Metodología: Los datos utilizados para este estudio fueron recopilados de los operadores de ELEPCO S.A. en base a los ańos 2008 - 2020. Las variables de entrada fueron la fecha y la energía generada para elaborar diferentes ca= sos con distintas condiciones con el fin de llegar a un modelo de Red Neurona= l Recurrente exitoso posible. Resultados: Una vez comprendidas las variables del model= o, los datos se dividieron en dos grupos: entrenamiento 70% y validación 30% respectivamente. Para el entrenamiento correspondiente se utilizó el algoritmo ADAM y las librerías proporcionadas por Python. <= /span>Área de estudio general: Ingeniería. Área de estudio específic= a: Eléctrica.
Keywords: : ADAM, Python, GRU, prediction, neural netwo= rks.	<= span style=3D'mso-bookmark:_Toc139953017'>	Abstract<= o:p> The prediction of events has been, since ancient times, a phenomenon capable of generating curiosity in human beings. Howe= ver, to achieve a projection of a future event, a detailed analysis of data is required to predict subsequent events. Objectives: The objective of the research was to develop two prediction systems using artificial neural networks and GRU to determine the predicted electrical generation at the ILLUCHI HYDROELECTRIC PLANT. Methodology: The data used for this study we= re collected from ELEPCO S.A. operators based on the years 2008 to 2020. The input variables were the date and the energy generated to develop differe= nt cases with different conditions to arrive at a successful Recurrent Neural Network model. Results: Once the variables of the model were understood, = the data was divided into two groups: training 70% and validation 30% respectively. The ADAM algorithm and libraries provided by Python were us= ed for the corresponding training. = General area of study: Engineering. = Specific area of study: Electrical.

Introducción

La generación de electricidad es crucial, lo que se evidencia en la amplia gam= a de estudios relacionados con la investigación en este campo. Aunque se sabe qu= e la generación de electricidad tiene un comportamiento no lineal y variable, es= to motiva a los investigadores a adaptar sus metodologías para obtener mejores resultados. Hay modelos predictivos para la producción de electricidad que utilizan técnicas de aprendizaje computacional para mejorar el uso del agua= de los ríos como recurso hídrico (Castillo et al., 2022a).

Se ha observado el uso de técnicas y modelos basados en aprendizaje automático, como las redes neuronales artificiales, para obtener mejores resultados en comparación con técnicas tradicionales como la regresión lineal y ARIMA (Castillo et al., 2022b). Estos modelos se aplican en la predicción de la generación de energía y otros fenómenos estocásticos. La importancia del aprendizaje automático radica en la precisión de las predicciones, los márg= enes de error y los valores finales, debido a su capacidad para seleccionar variables relevantes. El aprendizaje automático es un tipo de análisis de d= atos que automatiza la construcción de modelos analíticos y es una rama de la inteligencia artificial basada en el aprendizaje a partir de datos. Su obje= tivo es identificar patrones y tomar decisiones con respecto a variables sin intervención humana (Ti et al., 2021; Bunnoon, 2011, p. 2). Al utilizar un modelo de predicción en la generación de energía, podremos identificar patr= ones de comportamiento en los generadores 1 y 2 de la Central Hidroeléctrica ILLUCHI. Estos valores y gráficos nos permitirán hacer recomendaciones de u= so o detectar problemas en la generación. Se propone comparar el comportamiento = de la generación utilizando dos modelos diferentes: una unidad recurrente cerr= ada y una LSTM. Estos modelos serán evaluados a través de sus resultados en gráficos y su capacidad para manejar y seleccionar variables y datos relevantes.

Lo expuesto por Eseye & Tukia (2019), las redes neuronales artificiales, como las GRU y las LSTM, utilizan enfoques diferen= tes para controlar la información y evitar el problema del gradiente de desaparición. Estos son los principales puntos de comparación entre estos d= os métodos (Sahagún, 2018, p. 119). La unidad GRU controla el flujo de informa= ción como la unidad LSTM, pero sin tener que utilizar una unidad de memoria, simplemente expone el contenido oculto completo sin ningún control mientras= que las GRU son relativamente nuevas y, según la experiencia, su rendimiento es similar al de las LSTM, pero computacionalmente más eficientes (como se seń= aló, tienen una estructura menos compleja) (Aguilar & Paredes, 2018; Illidge= et al., 2020). Una vez que se han analizado las variables del modelo, los dato= s se dividen en tres grupos: entrenamiento, prueba y validación. El 80% de los valores recolectados se utilizan para el entrenamiento y el 20% restante pa= ra la validación final del modelo planteado. Las GRUs, o Unidades de Corrientes Cerradas, son una variación del diseńo de las RNN y utilizan un proceso de compuerta para gestionar y controlar el flujo de automatización entre las células de la red neuronal. Esto permite a las GRUs capturar dependencias s= in ignorar la información pasada en grandes conjuntos de datos secuenciales.

Además Ramírez (2013), sostiene que la principal diferencia entre una GRU y un LST= M es que una GRU tiene dos puertas (puertas de reinicio y actualización) mientras que un LSTM tiene tres puertas (puertas de entrada, salida y olvido). =

Objetivo General: Desarrollar un sistema de predicción utilizando redes neuronales artificiales y unidades recurrentes cerradas en Python para predecir la generación de energía eléct= rica en la CENTRAL HIDROELECTRICA ILLUCHI.

Objetivos Específicos: Obtener información actualizada sobre el desarrollo de algoritmos utilizados en la predicción de generación eléctrica en centrales hidroeléctricas.<= span style=3D'font-family:"Times New Roman",serif;color:windowtext'><= /span>

Determinar los parámetros necesarios para el desarrollo de algoritmos a implementar en= la red neuronal recurrente (RNN) y la unidad recurrente cerrada (GRU)

Implementar la RNN y GRU sobre el software Python

Validar los resultados obtenidos de la red neuronal artificial y las unidades recurrentes cerradas.

Metodología

Esta investigación examina la situación del sistema de generación en relación con la predicción a corto p= lazo de la demanda de energía eléctrica en la CENTRAL HIDROELÉCTRICA ILLUCHI 2. = Se considera una gran cantidad de información sobre la demanda de energía eléc= trica en diferentes momentos en que la central está en operación. Se proponen var= ias alternativas para analizar las variaciones en la demanda de energía eléctri= ca.

Para comprender mejor la situación, fenómeno u objeto de estudio, es importante descomponer sus características en sus diversos rasgos. En este caso, se realiza un reconocimiento de hechos e ideas para definir el comportamiento = en la CENTRAL HIDROELÉCTRICA ILLUCHI 2. Se recopila una base de datos histórica con valores de corriente, potencia y demanda de energía eléctrica, y se crea una vista previa del funcionamiento de la central. Esto permite un mejor entendimiento técnico de la situación.

En esta p= arte de la investigación aplicativa, se utilizarán todos los conocimientos técni= cos y científicos adquiridos para desarrollar análisis y programar un algoritmo= en el lenguaje de programación Python. Este algoritmo permitirá realizar el pronóstico de la demanda de energía eléctrica y obtener resultados que se aproximen a los valores reales.

Resultados<= /span>

Generación de Energía eléctrica

La producción de electricidad implica la conversión de una forma de energía, c= omo la química, cinética, térmica, lumínica, nuclear o solar, en energía eléctr= ica (Eseye & Tukia, 2019, p. 18). Las centrales eléctricas son instalaciones que realizan estas transformaciones para la generación industrial de electricidad y son el primer paso en el sistema de suministro eléctrico (Ca= rrera, 2020, p. 1). La producción de electricidad se lleva a cabo principalmente a través de un generador eléctrico. Aunque todos los generadores funcionan co= n el mismo principio, difieren en la forma en que se accionan (GreenLux, 2021).<= o:p>

Una central hidroeléctrica es una instalación que genera electricidad aprovecha= ndo la energía potencial del agua almacenada en una presa a mayor altura que la central (Aguilar & Paredes, 2018). El agua se transporta a través de una tubería hasta la sala de máquinas, donde se utiliza para mover grandes turb= inas hidráulicas que generan electricidad en alternadores. Después de pasar por = las turbinas, el agua regresa a su cauce natural (Caja, 2020, p. 6). Las dos características principales de una central hidroeléctrica, desde el punto de vista de su capacidad de generación de electricidad son:

La potencia de una central hidroeléctrica depende del desnivel entre el nivel medio del embalse y el n= ivel medio de las aguas debajo de la central, así como del caudal máximo que pue= de pasar por las turbinas. También influyen las características de la turbina y del generador. La energía que se puede garantizar en un período determinado, generalmente un ańo, depende del volumen útil del embalse, de la cantidad de lluvia anual y de la potencia instalada (Cueva, 2018, pp. 64-68).

Unidades recurrentes cerradas

La GRU también conocida como “Gated Recurrent unit”, fue desarrollada para resolver problemas comunes de gradientes desvanecidos, además se considera = a la GRU como una variante avanzada de la LSTM, esto debido a sus diseńos simila= res y a sus excelentes resultados.

Las GRUs utilizan la puerta de actualización y la puerta de reinicio para resol= ver el problema del gradiente de fuga de una RRN estándar (Del Canto, 2019). Es= encialmente, se trata de dos vectores que determinan el tipo de información que se trans= mite a la salida. Lo que hace que estos vectores sean especiales es que pueden s= er entrenados por programadores para almacenar información, incluso informació= n de hace mucho tiempo.

= Figura = 1

Estructura del sistema GRU

<= /o:p>

Redes neuron= ales RecurrentesUna red LSTM es capaz de recordar información relevante en una secuencia y preservarla durante varios momento= s en el tiempo (Nieve, 2021, p. 130)<= span style=3D'font-size:12.0pt;line-height:115%;mso-bidi-font-family:"Times New = Roman"; font-weight:normal'>.=

=

=

=

= Figura = 2

Estructura del sistema LSTM

El proyecto tiene como objetivo desarrollar dos modelos = de predicción para mini centrales hidroeléctricas en Ecuador. Para ello, se ha tomado como referencia la minicentral eléctrica Illuchi 2, ubicada en la provincia de Cotopaxi, en el cantón Latacunga. Esto se logra mediante el us= o de modelos matemáticos y el entrenamiento de redes neuronales utilizando datos históricos de potencia desde 2018 hasta 2023, registrados diariamente. Esto permite tener un registro preciso de la cantidad de agua que pasa por la central en un período de tiempo, cumpliendo con los requisitos legales de la normativa INEN 59:2012 y asegurando una operación adecuada y sostenible de = la central. Además, se valida su efectividad con los datos reales obtenidos y = su respectivo porcentaje de error (Aguilar & Paredes, 2018, p. 12).=

GRU =

Este modelo se utilizó debido a su capacidad para procesar en profundidad y capt= urar relaciones de tendencias no lineales y patrones complejos de datos. Es recurrente en este tipo de proyectos y puede recordar información a largo p= lazo en los datos de caudales. Los datos históricos que se manejan pueden tener vacíos en su secuencia, pero con este modelo, los errores son minimizados ya que puede predecir lagunas de datos. Se considera una variante avanzada de = LSTM debido a sus diseńos similares y excelentes resultados, ya que combina las compuertas de olvido y entrada en una única actualización. La mención a esta red neuronal en este documento no es caprichosa, ya que se ha demostrado que GRU es mejor en algunas aplicaciones en comparación con la versión básica de LSTM. Además, GRU surge como una optimización de LSTM en cuanto al número de parámetros, ya que no tiene una unidad de memoria como tal.

La ecuación para la update gate es la siguiente:

reset gate:

La compuerta de reinicio (reset gate) se combina con el estado anterior (ht-1) y se aplica la función tangente hiperbólica (tanh) p= ara determinar qué información se va a reiniciar.

= Figura = 3

Representación de la celda GRU y sus operaciones internas

Por último, la utilización de una media exponencial nos permite combinar dicho resultado anterior con el estado pre= vio:

GRU, al igu= al que LSTM tiene una matriz de pesos para cada tipo de compuerta, donde Wu son los pesos para la update gate, Wr los pesos para la reset gate y Wh los pes= os de la combinación del update con la entrada actual.

Discusión

En esta sección se muestran los resultados del entrenamiento y validación de la comparación entre GRU y LSTM, propuesta y creada mediante el lenguaje de programación Python. Para el aprendizaje y validación de la RNA, se utilizan datos diarios. Además, se compara la predicción de la generación eléctrica utilizando redes neuronales artificiales con la que existe actualmente en ELEPCO S.A., que es una regresión lineal simple. Esto se hace para demostra= r la eficacia de la RNA para esta aplicación. Finalmente, se presentan los resultados de la predicción de la generación eléctrica.
Análi= sis de correlación de la información de la central hidroeléctrica Illuchi<= o:p>

Uno de los desafío= s al depurar la base de datos original es determinar si las variables tienen un comportamiento similar, es decir, si están correlacionadas entre sí. Como se muestra en la tabla, las variables de ańo, mes, día, hora y demanda de ener= gía eléctrica tienen un comportamiento similar, lo que indica que están correlacionadas

= Tabla 1

= Variables usadas para la predicción de la demanda de energía Eléctrica

DIA

MES

AŃO

G1

G2

TOTAL

D.MAXIMA

DIA

1.000.000

0,011726

-0,006109

-0,012916

-0,028274

-0,027828

-0,019256

MES

0,011726

1.000.000

0,042998

-0,100125

0,069436

-0,014784

-0,000606

AŃO

-0,006109

0,042998

1.000.000

0,114879

0,140095

0,169962

0,001297

G1

-0,012916

-0,100125

0,114879

1.000.000

0,13421

0,723899

0,062664

G2

-0,028274

0,069436

0,140095

0,13421

1.000.000

0,780818

0,076846

TOTAL

-0,027828

-0,014784

0,169962

0,723899

0,780818

1.000.000

0,093008

D.MAXIMO

-0,019256

-0,000606

0,001297

0,062664

0,076846

0,093008

1.000.000

Una for= ma de visualizar la correlación entre variables es mediante un mapa de calor, com= o se muestra en la figura 4.

Figura 4

Mapa de calor para verificar la correlación entre las variables de entrada

Un mapa de calor muestra las relaciones entre múltiples variables. Las áreas marcadas en verde indican q= ue las variables están fuertemente relacionadas, lo que es una de las principa= les razones por las que se seleccionaron estas variables para realizar la predicción. Además, el mapa de calor permite analizar los grupos generados = en relación con el comportamiento de la demanda de energía eléctrica. Por ejem= plo, en el caso del generador 1, se observó que este grupo generador consume una gran parte de la energía eléctrica total generada por la CENTRAL HIDROELÉCT= RICA ILLUCHI

En la tabla 2, se muestra la predicción de la generación= de energía utilizando el método de unidades recurrentes cerradas (GRU) y se calculan los errores correspondientes. Se observa que este modelo presenta = un error del 3,94% según el cálculo del error porcentual absoluto medio (MAPE)= .

Tabla 2

n

Mes

Producción Real

P. Pronosticada

Error Abs.

Error % Abs.

∑ Error Abs.

MAD

Error Nor.

∑ Error Nor.

TS

1=

Enero

38012,90

35391,93

2620,970784

6,89%

2620,970784

2621,0

2620,970784

2620,970784

1

2=

Febrero

27942,86

28712,17

769,3139497

2,75%

3390,284734

1695,1

-769,3139497

1851,656835

1,1

3=

Marzo

42541,94

45104,51

2562,5742

6,02%

5952,858935

1984,3

-2562,5742

-710,9173658

-0,4

4=

Abril

68120,00

66884,09

1235,909896

1,81%

7188,76883

1797,2

1235,909896

524,9925301

0,3

5=

Mayo

76645,16

78033,00

1387,843498

1,81%

8576,612328

1715,3

-1387,843498

-862,8509679

-0,5

6=

Junio

81080,00

77599,43

3480,573568

4,29%

12057,1859

2009,5

3480,573568

2617,7226

1,3

7=

Julio

95612,90

96436,25

823,3477822

0,86%

12880,53368

1840,1

-823,3477822

1794,374818

1

8=

Agosto

83187,10

85284,15

2097,05494

2,52%

14977,58862

1872,2

-2097,05494

-302,680122

-0,2

9=

Septiembre

66520,00

73563,95

7043,954036

10,59%

22021,54265

2446,8

-7043,954036

-7346,634158

-3

10

Octubre

58916,13

59300,17

384,0447329

0,65%

22405,58739

2240,6

-384,0447329

-7730,678891

-3,5

11

Noviembre

86640,00

81753,56

4886,436849

5,64%

27292,02424

2481,1

4886,436849

-2844,242042

-1,1

12

Diciembre

78037,50

80734,11

2696,605469

3,46%

29988,6297

2499,1

-2696,605469

-5540,847511

-2,2

SUMA %

47,31%

n

12

MAPE

3,94%

Nota= : Predicción de la generación de energía mediante GRU

A continuación, en la figura 5= , se muestra el comportamiento de las curvas de la producción real vs la producc= ión pronosticada en GRU, donde se puede apreciar de mejor manera una similitud = alta entre las curvas y su distorsión en cada mes del ańo.

Fig= ura 5

Comp= ortamiento de la predicción real-pronosticada RGU

En la tabla 3. Se realizó la predicción de la generación mediante el método LSTM = y se calculó sus respectivos errores, en donde se observa que en este modelo exi= ste un error MAPE del 3,85%.

Tabla 3<= /p>
= Predicción de la generación de energía modelo RNA-LSTM

n<= /p>

Mes

Producción Real=

P. Pronosticada=

Error Abs.

Error % Abs.

∑ Error Abs.

MAD

Error Nor.

∑ Error Nor.

TS=

1<= /p>

Enero

38012,90<= /span>

36934,35<= /span>

1078,548387

2,84%

1078,548387

1078,5

1078,548387

1078,548387

1<= /p>

2<= /p>

Febrero <= /span>

27942,86<= /span>

26793,18<= /span>

1149,678571

4,11%

2228,226959

1114,1

1149,678571

2228,226959

2<= /p>

3<= /p>

Marzo

42541,94<= /span>

46493,97<= /span>

3952,033392

9,29%

6180,260351

2060,1

-3952,033392

-1723,806434

-0,8

4<= /p>

Abril

68120,00<= /span>

65723,87<= /span>

2396,133333

3,52%

8576,393684

2144,1

2396,133333

672,3268997

0,3

5<= /p>

Mayo

76645,16<= /span>

75459,42<= /span>

1185,740675

1,55%

9762,134359

1952,4

1185,740675

1858,067575

1<= /p>

6<= /p>

Junio

81080,00<= /span>

75375,15<= /span>

5704,851562

7,04%

15466,98592

2577,8

5704,851562

7562,919138

2,9

Tabla 3<= /p>
= Predicción de la generación de energía modelo RNA-LSTM (continuación)

n<= /span>

Mes

Producción Real

P. Pronosticada

Error Abs.

Error % Abs.

∑ Error Abs.

MAD

Error Nor.

∑ Error Nor.

TS=

7<= /p>

Julio

95612,90<= /span>

93997,70<= /span>

1615,204889

1,69%

17082,19081

2440,3

1615,204889

9178,124027

3,8

8<= /p>

Agosto

83187,10<= /span>

81698,16<= /span>

1488,935988

1,79%

18571,1268

2321,4

1488,935988

10667,06001

4,6

9<= /p>

Septiembre

66520,00<= /span>

70508,97<= /span>

3988,972396

6,00%

22560,09919

2506,7

-3988,972396

6678,087619

2,7

10=

Octubre

58916,13<= /span>

58815,58<= /span>

100,5516633

0,17%

22660,65086

2266,1

100,5516633

6778,639282

3<= /p>

11=

Noviembre=

86640,00<= /span>

79740,00<= /span>

6899,995182

7,96%

29560,64604

2687,3

6899,995182

13678,63446

5,1

12=

Diciembre=

78037,50<= /span>

78227,69<= /span>

190,1943359

0,24%

29750,84038

2479,2

-190,1943359

13488,44013

5,4

SUMA %

46,20%

n<= /p>

12=

MAPE

3,85%

En la figura 6 se muestra el comportamiento de las curvas de la producción rea= l vs la producción pronosticada en LSTM, donde se puede apreciar de mejor manera= una similitud alta entre las curvas y su distorsión en cada mes del ańo.

Figura 6

= Comportamiento de la predicción real – pronostic= ada LSTM

Nota<= /b>: Generación-Actual= VS LSTM

La figura 7 muestra la información recopilada en las tablas 1 y 2, representad= a en dos gráficos de barras que muestran el comportamiento de la Desviación Media Absoluta (MAD). Estos gráficos permiten evaluar la dispersión de los valores analizados en relación con los valores esperados y proporcionan una medida = de los errores promedio de los pronósticos. Según el análisis realizado, el mé= todo de Redes Neuronales Artificiales con memoria a largo plazo (RNA-LSTM) prese= nta un error inferior en comparación con el método de unidades recurrentes cerr= adas (GRU).

Figura 7

Comportamiento de la desviación media absoluta<= /p>

Se realizó un anál= isis en la figura 8, utilizando los resultados obtenidos de las predicciones de = los dos métodos empleados para validar la predicción de la generación de energía. Se puede observar que la predicción más precisa se logró mediante el uso del modelo matemático RNA-LSTM en el progr= ama Python, obteniendo un error porcentual absoluto medio del 3,85%, a diferenc= ia de las GRU donde se obtuvo un error del 3,94%. Como se mencionó previamente, este valor debe ser inferior al 5 %. Por lo tanto, se puede concluir que el análisis y la validación son adecuados.

Figura 8=

Comportamiento de demanda KW para su validación

Conclusiones

·      =    Las Redes Neuronales Artificiales (= RNA) son capaces de predecir con mayor precisión que las unidades recurrentes cerradas (GRU), ya que presentan un error del 3.85% en comparación con el 3= .94% de error de las GRU. Esto significa que las RNA son ligeramente más precisa= s en sus predicciones que las GRU. Sin embargo, es importante tener en cuenta qu= e la elección entre RNA y GRU puede depender de otros factores, como la longitud= de la secuencia de entrenamiento y la infraestructura disponible.

·      =    Las GRU generalmente se usan cuando tiene muestras de entrenamiento de secuencia larga y desea una precisión rá= pida y decente y tal vez en casos donde la infraestructura es un problema. Se prefieren los RNN cuando la longitud de la secuencia es mayor y existe un b= uen contexto. Los LSTM, cuando se entrenan con más datos, le brindan mejores resultados que los GRU.

·      =    Las unidades recurrentes cerradas (= GRU) suelen utilizarse cuando se tienen secuencias de entrenamiento largas y se desea obtener una precisión rápida y decente, especialmente en casos donde = la infraestructura es un problema. Por otro lado, las redes LSTM se prefieren cuando la longitud de la secuencia es mayor y hay un buen contexto disponib= le. Cuando se entrenan con más datos, las redes LSTM pueden proporcionar mejores resultados que las GRU.

·      =    Después de realizar un estudio investigativo y consultar varias referencias bibliográficas relacionadas co= n la predicción de la generación eléctrica, se desarrolló un programa computacio= nal en el lenguaje de programación Python para predecir la generación eléctrica utilizando redes neuronales recurrentes.

·      =    Al realizar un análisis exhaustivo = de la base de datos de la CENTRAL HIDROELÉCTRICA ILLUCHI, se identificaron las variables de entrada mediante la creación de un mapa de calor que muestra la correlación entre ellas. Con esta información, se definió la aplicación e implementación de la red neuronal.=

Referencias Bibliográficas

Aguilar, M., & Paredes, A. (2018). Incidencia de los proyectos emblemáticos de generación eléctri= ca ejecutados en el periodo 2007-2016 en la oferta eléctrica del país y su prospectiva al ańo 2030. http://repositorio.puce.edu.ec/handle/22000/15074

Bunnoon, P. (2011, August). Mid-Term Load Forecasting Based on Neural Network Algorithm: A Comparison of Models. IJCEE, 3(4), 600-605. doi:10.17706/IJCEE

Caja, O. (2020). Librería = Python para el aprendizaje y la implementación de redes neuronales. Universi= dad Politécnica de Valencia: https://riunet.upv.es/handle/10251/152226<= /p>
Carrera, I. (2020). Genera= ción de Energía. https://www.haimec.cl/generacion-de-energia/
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Cueva, J. (2018). Rediseńo y construcción de un sistema de filtro de agua automático y estudio para la disminución de la velocidad del agua en el canal. 65-67. Quito, Ecuador.<= o:p>

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Ramírez, A. (2013). Métodos utilizados para el pronóstico de generación eléctrica en sistemas de distribución. https://repositorio.utp.edu.co/server/api/core/bitstreams/ee2fb283-daa7-4= 987-b747-faa1e42e9711/content

Rodríguez, V. (2018). Conc= eptos básicos sobre redes neuronales. https://vincentblog.xyz/posts/conceptos-basicos-sobre-redes-neuronales

Sahagún, P. (2018). Aplica= ción de redes neuronales convolucionales y recurrentes al diagnóstico. https://oa.upm.es/50400/1/TFG_PABLO_RODRIGUEZ_SAHAGUN_ALESANCO.pdf

Ti, Z., Wei, X., & Mingming, Z. (2021, July). Artificial Neural Networks based wake model for power prediction of wind farm. Renewable Energy, = 172, 618-631. https://doi.org/10.1016/j.renene.2021.03.030

Conflicto= de intereses

No existe conflicto de intereses en relación con el artículo presentado.

El artículo que se publica es de exclusiva responsabilid= ad de los autores y no necesariamente reflejan el pensamiento de la Revista Alfa Publicaciones.

El artículo queda en propiedad de la revista y, por tant= o, su publicación parcial y/o total en otro medio tiene que ser autorizado por= el director de la Revista Alfa Publicaciones.

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	DIA	MES	AŃO	G1	G2	TOTAL	D.MAXIMA
DIA	1.000.000	0,011726	-0,006109	-0,012916	-0,028274	-0,027828	-0,019256
MES	0,011726	1.000.000	0,042998	-0,100125	0,069436	-0,014784	-0,000606
AŃO	-0,006109	0,042998	1.000.000	0,114879	0,140095	0,169962	0,001297
G1	-0,012916	-0,100125	0,114879	1.000.000	0,13421	0,723899	0,062664
G2	-0,028274	0,069436	0,140095	0,13421	1.000.000	0,780818	0,076846
TOTAL	-0,027828	-0,014784	0,169962	0,723899	0,780818	1.000.000	0,093008
D.MAXIMO	-0,019256	-0,000606	0,001297	0,062664	0,076846	0,093008	1.000.000

n	Mes	Producción Real	P. Pronosticada	Error Abs.	Error % Abs.	∑ Error Abs.	MAD	Error Nor.	∑ Error Nor.	TS
1=	Enero	38012,90	35391,93	2620,970784	6,89%	2620,970784	2621,0	2620,970784	2620,970784	1
2=	Febrero	27942,86	28712,17	769,3139497	2,75%	3390,284734	1695,1	-769,3139497	1851,656835	1,1
3=	Marzo	42541,94	45104,51	2562,5742	6,02%	5952,858935	1984,3	-2562,5742	-710,9173658	-0,4
4=	Abril	68120,00	66884,09	1235,909896	1,81%	7188,76883	1797,2	1235,909896	524,9925301	0,3
5=	Mayo	76645,16	78033,00	1387,843498	1,81%	8576,612328	1715,3	-1387,843498	-862,8509679	-0,5
6=	Junio	81080,00	77599,43	3480,573568	4,29%	12057,1859	2009,5	3480,573568	2617,7226	1,3
7=	Julio	95612,90	96436,25	823,3477822	0,86%	12880,53368	1840,1	-823,3477822	1794,374818	1
8=	Agosto	83187,10	85284,15	2097,05494	2,52%	14977,58862	1872,2	-2097,05494	-302,680122	-0,2
9=	Septiembre	66520,00	73563,95	7043,954036	10,59%	22021,54265	2446,8	-7043,954036	-7346,634158	-3
10	Octubre	58916,13	59300,17	384,0447329	0,65%	22405,58739	2240,6	-384,0447329	-7730,678891	-3,5
11	Noviembre	86640,00	81753,56	4886,436849	5,64%	27292,02424	2481,1	4886,436849	-2844,242042	-1,1
12	Diciembre	78037,50	80734,11	2696,605469	3,46%	29988,6297	2499,1	-2696,605469	-5540,847511	-2,2
				SUMA %	47,31%
				n	12
				MAPE	3,94%

n<= /p>	Mes	Producción Real=	P. Pronosticada=	Error Abs.	Error % Abs.	∑ Error Abs.	MAD	Error Nor.	∑ Error Nor.	TS=
1<= /p>	Enero	38012,90<= /span>	36934,35<= /span>	1078,548387	2,84%	1078,548387	1078,5	1078,548387	1078,548387	1<= /p>
2<= /p>	Febrero <= /span>	27942,86<= /span>	26793,18<= /span>	1149,678571	4,11%	2228,226959	1114,1	1149,678571	2228,226959	2<= /p>
3<= /p>	Marzo	42541,94<= /span>	46493,97<= /span>	3952,033392	9,29%	6180,260351	2060,1	-3952,033392	-1723,806434	-0,8
4<= /p>	Abril	68120,00<= /span>	65723,87<= /span>	2396,133333	3,52%	8576,393684	2144,1	2396,133333	672,3268997	0,3
5<= /p>	Mayo	76645,16<= /span>	75459,42<= /span>	1185,740675	1,55%	9762,134359	1952,4	1185,740675	1858,067575	1<= /p>
6<= /p>	Junio	81080,00<= /span>	75375,15<= /span>	5704,851562	7,04%	15466,98592	2577,8	5704,851562	7562,919138	2,9

n<= /span>	Mes	Producción Real	P. Pronosticada	Error Abs.	Error % Abs.	∑ Error Abs.	MAD	Error Nor.	∑ Error Nor.	TS=
7<= /p>	Julio	95612,90<= /span>	93997,70<= /span>	1615,204889	1,69%	17082,19081	2440,3	1615,204889	9178,124027	3,8
8<= /p>	Agosto	83187,10<= /span>	81698,16<= /span>	1488,935988	1,79%	18571,1268	2321,4	1488,935988	10667,06001	4,6
9<= /p>	Septiembre	66520,00<= /span>	70508,97<= /span>	3988,972396	6,00%	22560,09919	2506,7	-3988,972396	6678,087619	2,7
10=	Octubre	58916,13<= /span>	58815,58<= /span>	100,5516633	0,17%	22660,65086	2266,1	100,5516633	6778,639282	3<= /p>
11=	Noviembre=	86640,00<= /span>	79740,00<= /span>	6899,995182	7,96%	29560,64604	2687,3	6899,995182	13678,63446	5,1
12=	Diciembre=	78037,50<= /span>	78227,69<= /span>	190,1943359	0,24%	29750,84038	2479,2	-190,1943359	13488,44013	5,4
				SUMA %	46,20%
				n<= /p>	12=
				MAPE	3,85%