Comparative Analysis of Deep Learning Methods for Fault Avoidance and Predicting Demand in Electrical Distribution

Karla Schröder, Gonzalo Farias, Sebastián Dormido-Canto () and Ernesto Fabregas
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Karla Schröder: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso 2362804, Chile
Gonzalo Farias: Escuela de Ingeniería Eléctrica, Pontificia Universidad Católica de Valparaíso, Av. Brasil 2147, Valparaíso 2362804, Chile
Sebastián Dormido-Canto: Departamento de Informática y Automática, Uiversidad Nacional de Educación a Distancia, Juan del Rosal 16, 28040 Madrid, Spain
Ernesto Fabregas: Departamento de Informática y Automática, Uiversidad Nacional de Educación a Distancia, Juan del Rosal 16, 28040 Madrid, Spain

Energies, 2024, vol. 17, issue 11, 1-13

Abstract: In recent years, the distribution network in Chile has undergone various modifications to meet new demands and integrate new technologies. However, these improvements often do not last as long as expected due to inaccurate forecasting, resulting in frequent equipment changes and service interruptions. These issues affect project investment, unsold energy, and penalties for poor quality of supply. Understanding the electricity market, especially in distribution, is crucial and requires linking technical quality standards with service quality factors, such as the frequency and duration of interruptions, to understand their impact on regulated distribution to customers. In this context, a comparative study will be carried out between Long Short-Term Memory (LSTM) and transformer architectures, with the aim of improving the sizing of distribution transformers and preventing failures when determining the nominal power of the transformer to be installed. Variables such as voltages and operating currents of transformers installed between 2020 and 2021 in the Valparaíso region, Chile, along with the type and number of connected customers, maximum and minimum temperatures of the sectors of interest, and seasonality considerations will be used. The compilation of previous studies and the identification of key variables will help to propose solutions based on error percentages to optimise the accuracy of transformer sizing.

Keywords: neural networks; transformers; electricity consumption; estimation; energy efficiency (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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