Analysis and forecasting of electricity prices using an improved time series ensemble approach: an application to the Peruvian electricity market

Salvatore Mancha Gonzales; Hasnain Iftikhar; Javier Linkolk López-Gonzales; Salvatore Mancha Gonzales; Hasnain Iftikhar; Javier Linkolk López-Gonzales

doi:10.3934/math.20241067

AIMS Mathematics

2024, Volume 9, Issue 8: 21952-21971. doi: 10.3934/math.20241067

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Analysis and forecasting of electricity prices using an improved time series ensemble approach: an application to the Peruvian electricity market

1.
Escuela de Posgrado, Universidad Peruana Unión, Lima, Peru
2.
Department of Statistics, Quaid-i-Azam University, Islamabad 45320, Pakistan

Received: 28 March 2024 Revised: 06 June 2024 Accepted: 11 June 2024 Published: 11 July 2024
MSC : 62E15, 62G30

In today's electricity markets, accurate electricity price forecasting provides valuable insights for decision-making among participants, ensuring reliable operation of the power system. However, the complex characteristics of electricity price time series hinder accessibility to accurate price forecasting. This study addressed this challenge by introducing a novel approach to predicting prices in the Peruvian electricity market. This approach involved preprocessing the monthly electricity price time series by addressing missing values, stabilizing variance, normalizing data, achieving stationarity, and addressing seasonality issues. After this, six standard base models were employed to model the time series, followed by applying three ensemble models to forecast the filtered electricity price time series. Comparisons were conducted between the predicted and observed electricity prices using mean error accuracy measures, graphical evaluation, and an equal forecasting accuracy statistical test. The results showed that the proposed novel ensemble forecasting approach was an efficient and accurate tool for forecasting monthly electricity prices in the Peruvian electricity market. Moreover, the ensemble models outperformed the results of earlier studies. Finally, while numerous global studies have been conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast electricity prices for the Peruvian electricity market.
Citation: Salvatore Mancha Gonzales, Hasnain Iftikhar, Javier Linkolk López-Gonzales. Analysis and forecasting of electricity prices using an improved time series ensemble approach: an application to the Peruvian electricity market[J]. AIMS Mathematics, 2024, 9(8): 21952-21971. doi: 10.3934/math.20241067

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Abstract

In today's electricity markets, accurate electricity price forecasting provides valuable insights for decision-making among participants, ensuring reliable operation of the power system. However, the complex characteristics of electricity price time series hinder accessibility to accurate price forecasting. This study addressed this challenge by introducing a novel approach to predicting prices in the Peruvian electricity market. This approach involved preprocessing the monthly electricity price time series by addressing missing values, stabilizing variance, normalizing data, achieving stationarity, and addressing seasonality issues. After this, six standard base models were employed to model the time series, followed by applying three ensemble models to forecast the filtered electricity price time series. Comparisons were conducted between the predicted and observed electricity prices using mean error accuracy measures, graphical evaluation, and an equal forecasting accuracy statistical test. The results showed that the proposed novel ensemble forecasting approach was an efficient and accurate tool for forecasting monthly electricity prices in the Peruvian electricity market. Moreover, the ensemble models outperformed the results of earlier studies. Finally, while numerous global studies have been conducted from various perspectives, no analysis has been undertaken using an ensemble learning approach to forecast electricity prices for the Peruvian electricity market.

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