Deep Learning Applied to Univariate Electricity Consumption Time Series: A Systematic Literature Review

Marla T. B. Geller

¹ Laboratory of Computational Intelligence - LabIC – UFOPA- Santarém, PA, Brazil

² Federal Institute of Education, Science and Technology of Pará – IFPA- Santarém, PA, Brazil

³ Federal University of Western Pará - Institute of Geosciences and Engineering – IEG/UFOPA - Santarém, PA, Brazil

*Corresponding Author: Marla T. B. Geller, Email: [email protected]

This article presents a Systematic Literature Review (SLR) of studies applying Deep Learning (DL) models to forecast Electricity Consumption (EC) using univariate time series. After screening 2,800 articles through well-defined inclusion and exclusion criteria, 62 studies were selected for analysis. These studies were systematically organized to highlight DL architectures, performance metrics, preprocessing practices, and key methodological choices. The review uniquely focuses on univariate contexts—an underexplored but relevant scenario for energy forecasting, especially where data availability is limited. The paper identifies dominant trends, methodological gaps, and emerging challenges, offering a critical foundation for future research in the field.

Systematic Literature Review, Deep Learning, Univariate Time Series, Electricity Consumption Prediction, Energy Forecasting

Marla T. B. Geller, Davi Guimarães Silva, Anderson Alvarenga de Sousa Meneses (2025). Deep Learning Applied to Univariate Electricity Consumption Time Series: A Systematic Literature Review. Journal of Artificial Intelligence and Systems, 7, 11–34. https://doi.org/10.33969/AIS.2025070102.

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