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Energies
Volume 18
Issue 22
10.3390/en18225885
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Article

Deep Learning for Residential Electrical Energy Consumption Forecasting: A Hybrid Framework with Multiscale Temporal Analysis and Weather Integration

by
Bruno Knevitz Hammerschmitt
1,2,*,†,
Marcos Vinicio Haas Rambo
1,2,*,†,
Andre de Souza Leone
1,2,
Luciana Michelotto Iantorno
1,
Handy Borges Schiavon
3,4,
Dayanne Peretti Corrêa
3,4,5,
Paulo Lissa
3,4,5,
Marcus Keane
3,4,5 and
Rodrigo Jardim Riella
1,2
1
Future Grid, Lactec, Curitiba 80215-090, PR, Brazil
2
Department of Electrical Engineering, Federal University of Paraná, Curitiba 81531-980, PR, Brazil
3
College of Science and Engineering, University of Galway, H91 TK33 Galway, Ireland
4
Informatics Research Unit for Sustainable Engineering (IRUSE), H91 TK33 Galway, Ireland
5
Ryan Institute, University of Galway, H91 TK33 Galway, Ireland
*
Authors to whom correspondence should be addressed.
†
These authors contributed equally to this work.
Energies 2025, 18(22), 5885; https://doi.org/10.3390/en18225885 (registering DOI)
Submission received: 3 October 2025 / Revised: 2 November 2025 / Accepted: 6 November 2025 / Published: 8 November 2025
(This article belongs to the Section F5: Artificial Intelligence and Smart Energy)
Versions Notes

Abstract

This paper presents an evaluation of the use of deep learning architectures for forecasting electrical energy consumption in residential environments. The main contribution of this study lies in the development and assessment of a hybrid forecasting framework that integrates multiscale temporal analysis and weather data, enabling evaluation of predictive performance across different temporal granularities, forecast horizons, and aggregation levels. Single and hybrid models were compared, trained with high-resolution data from a single residence, both considering only endogenous variables and including exogenous variables (weather data). The results showed that, among all models tested in this study, the hybrid LSTM + GRU model achieved the highest predictive performance, with R2 values of 94.62% using energy data and 95.25% when weather variables were included. Intermediary granularities, particularly the 6 steps, offered the best balance between temporal detail and predictive robustness for the tests performed. Furthermore, short-time windows aggregation (1 to 5 min) showed better accuracy, while the inclusion of weather data in scenarios with larger aggregation windows and longer horizons provided additional gains. The results reinforce the potential of hybrid deep learning models as effective tools for forecasting residential electricity consumption, with possible practical applications in energy management, automation, and integration of distributed energy resources.
Keywords: electrical energy consumption forecasting; residential electricity consumption; deep learning; hybrid models; temporal granularity; multiscale; weather variables electrical energy consumption forecasting; residential electricity consumption; deep learning; hybrid models; temporal granularity; multiscale; weather variables

Share and Cite

MDPI and ACS Style

Hammerschmitt, B.K.; Rambo, M.V.H.; Leone, A.d.S.; Iantorno, L.M.; Schiavon, H.B.; Corrêa, D.P.; Lissa, P.; Keane, M.; Riella, R.J. Deep Learning for Residential Electrical Energy Consumption Forecasting: A Hybrid Framework with Multiscale Temporal Analysis and Weather Integration. Energies 2025, 18, 5885. https://doi.org/10.3390/en18225885

AMA Style

Hammerschmitt BK, Rambo MVH, Leone AdS, Iantorno LM, Schiavon HB, Corrêa DP, Lissa P, Keane M, Riella RJ. Deep Learning for Residential Electrical Energy Consumption Forecasting: A Hybrid Framework with Multiscale Temporal Analysis and Weather Integration. Energies. 2025; 18(22):5885. https://doi.org/10.3390/en18225885

Chicago/Turabian Style

Hammerschmitt, Bruno Knevitz, Marcos Vinicio Haas Rambo, Andre de Souza Leone, Luciana Michelotto Iantorno, Handy Borges Schiavon, Dayanne Peretti Corrêa, Paulo Lissa, Marcus Keane, and Rodrigo Jardim Riella. 2025. "Deep Learning for Residential Electrical Energy Consumption Forecasting: A Hybrid Framework with Multiscale Temporal Analysis and Weather Integration" Energies 18, no. 22: 5885. https://doi.org/10.3390/en18225885

APA Style

Hammerschmitt, B. K., Rambo, M. V. H., Leone, A. d. S., Iantorno, L. M., Schiavon, H. B., Corrêa, D. P., Lissa, P., Keane, M., & Riella, R. J. (2025). Deep Learning for Residential Electrical Energy Consumption Forecasting: A Hybrid Framework with Multiscale Temporal Analysis and Weather Integration. Energies, 18(22), 5885. https://doi.org/10.3390/en18225885

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