TY - GEN

T1 - Simple Exponential Smoothing for Forecasting the Numbers of Pole-Mounted Transformer Failures

AU - Mbuli, Nhlanhla

AU - Pretorius, Jan Harm C.

N1 - Publisher Copyright:
© 2023 IEEE.

PY - 2023

Y1 - 2023

N2 - Pole-mounted transformers form the vital link between the electric utility company and the final consumer of electricity. In this paper, the authors use the simple exponential smoothing (SES) forecasting technique to forecast the quarterly numbers of pole-mounted transformer failures. The value of the smoothing constant, α, to be used in the forecast, is found by first formulating a non-linear programming problem (NLP) problem to minimize the forecast root mean square error (RMSE). Then, a software code that implements exhaustive search algorithm to solve the NLP is written in Python. The SES forecast is compiled and compared to the multiplicative and additive decomposition forecasting methods. Various measures of error, including mean absolute deviation (MAD), mean absolute percentage error (MAPE), mean squared error (MSE), and RMSE. It was found that, irrespective of the measure of error considered, the SES forecast was always outperformed by either of the decomposition methods.

AB - Pole-mounted transformers form the vital link between the electric utility company and the final consumer of electricity. In this paper, the authors use the simple exponential smoothing (SES) forecasting technique to forecast the quarterly numbers of pole-mounted transformer failures. The value of the smoothing constant, α, to be used in the forecast, is found by first formulating a non-linear programming problem (NLP) problem to minimize the forecast root mean square error (RMSE). Then, a software code that implements exhaustive search algorithm to solve the NLP is written in Python. The SES forecast is compiled and compared to the multiplicative and additive decomposition forecasting methods. Various measures of error, including mean absolute deviation (MAD), mean absolute percentage error (MAPE), mean squared error (MSE), and RMSE. It was found that, irrespective of the measure of error considered, the SES forecast was always outperformed by either of the decomposition methods.

KW - Exhaustive search

KW - Python

KW - pole-mounted transformer

KW - root mean square error

KW - simple exponential smoothing

UR - http://www.scopus.com/inward/record.url?scp=85177660618&partnerID=8YFLogxK

U2 - 10.1109/AFRICON55910.2023.10293359

DO - 10.1109/AFRICON55910.2023.10293359

M3 - Conference contribution

AN - SCOPUS:85177660618

T3 - IEEE AFRICON Conference

BT - Proceedings of the 16th IEEE AFRICON, AFRICON 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 16th IEEE AFRICON, AFRICON 2023

Y2 - 20 September 2023 through 22 September 2023

ER -