Neural networks for demand forecasting in primary power distribution systems

Abimael Diaz; José Luis Pruna; Xavier Proaño; Carlos Quinatoa

Authors

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.

Keywords:

Artificial, Networks, Model, LSTM, Optimizer, Distribution

Abstract

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

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.

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