Limitations of the LSTM Model in Electricity Demand Forecasting with Low Volume Simulated Data
DOI:
https://doi.org/10.70577/ASCE/240.260/2025Keywords:
Electricity demand, LSTM, ARIMA, machine learning.Abstract
The analysis evaluated the LSTM model to predict electricity demand, using a simulated set of 1000 points (42 days) with variables such as temperature, solar radiation and type of consumer (residential/industrial). The results show poor performance, with negative R² values (-0.0043, -0.1380, -0.0849) and high RMSE (29.23, 31.68, 30.52), indicating less accurate predictions than the average data. The model configuration (50 neurons, look_back=24) proved unstable, probably due to the limited size of the data set and the inability to capture relationships between variables and electrical demand. The literature highlights that LSTM models require large data volumes and careful optimization to be effective. Compared to ARIMA, a traditional statistical method, LSTM failed to capture complex temporal patterns. Challenges include data quality, model complexity, and integration with legacy systems. It is suggested to increase the size of the data set, adjust the model architecture and explore hybrid approaches (LSTM+ARIMA) to improve accuracy. This analysis highlights the importance of aligning model complexity with available data to optimize energy management in smart grids.
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Copyright (c) 2025 Christian Paul Reyes Orozco, Cristian Luis Inca Balseca
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