Evaluating the Impact of Frequency Decomposition Techniques on LSTM-Based Household Energy Consumption Forecasting

Maissa Taktak and Faouzi Derbel ()
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Maissa Taktak: Smart Diagnostic and Online Monitoring, Leipzig University of Applied Sciences, Wächterstraße 13, 04107 Leipzig, Germany
Faouzi Derbel: Smart Diagnostic and Online Monitoring, Leipzig University of Applied Sciences, Wächterstraße 13, 04107 Leipzig, Germany

Energies, 2025, vol. 18, issue 10, 1-31

Abstract: Accurate energy consumption forecasting is essential for efficient power grid management, yet existing deep learning models struggle with the multi-scale nature of energy consumption patterns. Contemporary approaches like LSTM and GRU networks process raw time series directly, failing to distinguish between distinct frequency components that represent different physical phenomena in household energy usage. This study presents a novel methodological method that systematically decomposes energy consumption signals into low-frequency components representing gradual trends and daily routines and high-frequency components capturing transient events, such as appliance switching, before applying predictive modeling. Our approach employs computationally efficient convolution-based filters—uniform and binomial—with varying window sizes to separate these components for specialized processing. Experiments on two real-world datasets at different temporal resolutions (1 min and 15 min) demonstrate significant improvements over state-of-the-art methods. For the Smart House dataset, our optimal configuration achieved an R² of 0.997 and RMSE of 0.034, substantially outperforming previous models with R² values of 0.863. Similarly, for the Mexican Household dataset, our approach yielded an R² of 0.994 and RMSE of 13.278, compared to previous RMSE values exceeding 82.488. These findings establish frequency decomposition as a crucial preprocessing step for energy forecasting as it significantly improve the prediction in smart grid applications.

Keywords: energy forecasting; frequency decomposition; binomial filter; uniform filter; window size; short-term fluctuations; long-term trends; smart grid; power consumption; time series analysis (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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