March 3, 2026

Deep learning approach to the spot price of electricity in Colombia

Key Points

Spot price forecasting is essential for evaluating financial outcomes, as it helps in informed decision-making for investments and risk management.
Deep learning methods exhibit the ability to capture trends in electricity pricing, outperforming traditional methods like ARIMA in certain aspects.
Statistical analysis using Box-Jenkins approaches still holds better predictive accuracy compared to deep learning methods in this context.
Findings highlight the need for further refinement in deep learning applications for improved forecasting in dynamic energy markets.

Abstract

The electricity price forecast is relevant data for evaluating the financial results of operating companies, assessing new investment projects, and structuring hedging portfolios. This paper analyzes the effectiveness of spot price forecasting methods for electric power in Colombia based on Deep Learning and their ability to overcome an ARIMA-type structure. The results are input for researchers and analysts of global electricity markets who are interested in making projections of electric power and some variables that have mean reversion, high volatility, and jump characteristics. Deep Learning methods can capture the series trends in out-of-sample periods. However, they cannot beat the performance of forecasts made by applying Box-Jenkins approaches.

Deep learning approach to the spot price of electricity in Colombia

Key Points

Abstract

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