Optimization of Smart Home Energy Consumption Using Machine Learning-Based Load Forecasting

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Comparison of RF and LSTM models shows superior forecasting accuracy for household energy consumption.

LSTM demonstrates a lower mean absolute error (3.2) than the RF model (6.5), highlighting its forecasting superiority.
During peak load conditions, LSTM achieved 89.7% accuracy compared to RF's 72.4%, showcasing its adaptability.
Studying both models involved quantitative analysis using a publicly available dataset, including preprocessing steps.
Results support the use of deep learning techniques in real-time energy forecasting for smart home systems.

Rudiyanto et al. (2025) studied this question.

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View Full Paper

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Comparison of RF and LSTM models shows superior forecasting accuracy for household energy consumption.

LSTM demonstrates a lower mean absolute error (3.2) than the RF model (6.5), highlighting its forecasting superiority.
During peak load conditions, LSTM achieved 89.7% accuracy compared to RF's 72.4%, showcasing its adaptability.
Studying both models involved quantitative analysis using a publicly available dataset, including preprocessing steps.
Results support the use of deep learning techniques in real-time energy forecasting for smart home systems.

Rudiyanto et al. (2025) studied this question.

View Full Paper

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Bookmark