Neural networks for demand forecasting in primary power distribution systems

Abimael Diaz; Jhon Pruna; Xavier Proaño; Carlos Quinatoa

Autores/as

Diaz Abimael Department of Electrical Engineering, University Technical of Cotopaxi. Latacunga, Ecuador.
Pruna Jhon Department of Electrical Engineering, University Technical of Cotopaxi. Latacunga, Ecuador.
Proaño Xavier Department of Electrical Engineering, University Technical of Cotopaxi. Latacunga, Ecuador.
Quinatoa Carlos Department of Electrical Engineering, University Technical of Cotopaxi. Latacunga, Ecuador.

Palabras clave:

Redes artificiales, modelo, LSTM, optimizador, distribución

Resumen

DOI: https://doi.org/10.46296/ig.v8i16.0282

Resumen

Se desarrolló un modelo predictivo para analizar los perfiles de carga en redes de distribución eléctrica mediante redes neuronales artificiales. Se empleó una metodología mixta de análisis de datos, que combina enfoques cualitativos y cuantitativos. La recopilación de datos se realizó a intervalos de 15 minutos, generando 5856 registros de kilovatios (kW), kilovoltamperios (kVA) y kilovoltamperios reactivos (kVAr). El modelo se dividió en un 70 % de datos para entrenamiento (4099,2 registros) y un 30 % para validación (1756,8 registros). La arquitectura del modelo consta de tres capas: una capa de entrada, capas ocultas para análisis y una capa de salida. Se utilizó un modelo secuencial con una capa de entrada LSTM de 200 neuronas y una capa de salida densa con una sola neurona. El optimizador Adam y la medida de pérdidas mse se utilizaron para el entrenamiento. La función de activación utilizada fue lineal. Finalmente, el modelo demostró un error de pronóstico de ±3,57 % para el modelo 2 y de ±4,38 % para el modelo 3, lo que lo hace eficaz para la planificación eléctrica.

Palabras clave: Redes artificiales, modelo, LSTM, optimizador, distribución.

Abstract

A predictive model was developed to analyze load profiles in electric power distribution networks using artificial neural networks. A mixed data analysis methodology, combining qualitative and quantitative approaches. Data collection was performed at 15-minute intervals, generating 5856 kilowatt (kW), kilovolt-ampere (kVA) and kilovolt ampere reactive (kVAr) records. The model was divided into 70% data for training (4099.2 records) and 30% for validation (1756.8 records). The model architecture consists of three layers: an input layer, hidden layers for analysis and an output layer. A sequential model with a 200 - neuron LSTM input layer and a dense output layer with a single neuron was used. The optimizer Adam and the loss measure mse were used for training. The activation function used was linear. Finally, the model demonstrated a forecast error of ± 3.57% for model 2 and ± 4.38% for model 3, making it effective for electrical planning.

Keywords: Artificial, Networks, Model, LSTM, Optimizer, Distribution.

Información del manuscrito:
Fecha de recepción: 15 de abril de 2025.
Fecha de aceptación: 27 de junio de 2025.
Fecha de publicación: 10 de julio de 2025.

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