Version 1
: Received: 14 May 2024 / Approved: 15 May 2024 / Online: 15 May 2024 (10:49:00 CEST)
How to cite:
Alizadegan, H.; Radmehr, A.; Karimi, H.; Asghari Ilani, M. Comparative Study of Long Short-Term Memory (LSTM), Bidirectional LSTM, and Traditional Machine Learning Approaches for Energy Consumption Prediction. Preprints2024, 2024051020. https://doi.org/10.20944/preprints202405.1020.v1
Alizadegan, H.; Radmehr, A.; Karimi, H.; Asghari Ilani, M. Comparative Study of Long Short-Term Memory (LSTM), Bidirectional LSTM, and Traditional Machine Learning Approaches for Energy Consumption Prediction. Preprints 2024, 2024051020. https://doi.org/10.20944/preprints202405.1020.v1
Alizadegan, H.; Radmehr, A.; Karimi, H.; Asghari Ilani, M. Comparative Study of Long Short-Term Memory (LSTM), Bidirectional LSTM, and Traditional Machine Learning Approaches for Energy Consumption Prediction. Preprints2024, 2024051020. https://doi.org/10.20944/preprints202405.1020.v1
APA Style
Alizadegan, H., Radmehr, A., Karimi, H., & Asghari Ilani, M. (2024). Comparative Study of Long Short-Term Memory (LSTM), Bidirectional LSTM, and Traditional Machine Learning Approaches for Energy Consumption Prediction. Preprints. https://doi.org/10.20944/preprints202405.1020.v1
Chicago/Turabian Style
Alizadegan, H., Hossein Karimi and Mohsen Asghari Ilani. 2024 "Comparative Study of Long Short-Term Memory (LSTM), Bidirectional LSTM, and Traditional Machine Learning Approaches for Energy Consumption Prediction" Preprints. https://doi.org/10.20944/preprints202405.1020.v1
Abstract
Responsible, efficient, and environmentally conscious energy consumption practices are increasingly essential for ensuring the reliability of the modern electricity grid. This study focuses on leveraging time series analysis to improve forecasting accuracy, crucial for various application domains where real-world time series data often exhibit complex, non-linear patterns. Our approach advocates for utilizing Long Short Term Memory (LSTM) and Bidirectional LSTM (BiLSTM) models for precise time series forecasting. To ensure a fair evaluation, we compare the performance of our proposed approach with traditional neural networks, time-series forecasting methods, and conventional decline curves. Additionally, individual models based on LSTM, Bi-LSTM, and other machine learning methods are implemented for a comprehensive assessment. Experimental results consistently demonstrate that our proposed model outperforms all benchmarking methods in terms of mean absolute error (MAE) across most datasets. Addressing the imbalance between activations by consumer and prosumer groups, our predictions show superior performance compared to several traditional forecasting methods, such as the Autoregressive Integrated Moving Average model (ARIMA) and Seasonal Autoregressive Integrated Moving Average model (SARIMA). Specifically, the root mean square error (RMSE) of Bi-LSTM is 5.35%, 46.08%, and 50.6% lower than LSTM, ARIMA, and SARIMA, respectively, on the May test data.
Keywords
Time series forecasting; LSTM; Bi-LSTM; Deep learning; ARIMA; SARIMA; Energy
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
