Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids
Accurate load forecasting is essential for ensuring the stability and efficiency of modern power systems, particularly in the context of increasing renewable energy integration. This study proposes an advanced forecasting approach using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LS...
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2025-01-01
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| author | Muhammad Asghar Majeed Sotdhipong Phichaisawat Furqan Asghar Umair Hussan |
| author_facet | Muhammad Asghar Majeed Sotdhipong Phichaisawat Furqan Asghar Umair Hussan |
| author_sort | Muhammad Asghar Majeed |
| collection | DOAJ |
| description | Accurate load forecasting is essential for ensuring the stability and efficiency of modern power systems, particularly in the context of increasing renewable energy integration. This study proposes an advanced forecasting approach using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) to enhance electricity demand prediction. The research analyzes key temporal patterns in historical load, renewable energy generation, and weather data to improve forecasting precision. RNN-based LSTM, with its gated memory structure, effectively captures long-term dependencies and mitigates vanishing gradient issues, making it highly suitable for modeling complex time-series data in power systems. Its ability to retain crucial past information across extended sequences enables precise load forecasting, even in the presence of fluctuations and intermittent renewable energy sources. To validate the effectiveness of the proposed technique, the model was implemented and tested using MATLAB/Simulink, ensuring realistic simulation of power system dynamics. Through a comprehensive comparative analysis, RNN-LSTM is found to exhibit superior predictive accuracy, achieving the lowest RMSE (2.2889), MAE (1.1041), and MAPE (1.538%), while also demonstrating faster convergence, lower inference time, and higher stability. Furthermore, LSTM demonstrates superior convergence speed, lower inference time (0.00098 s), and high stability (0.9091), which confirms its robustness. Compared to other deep learning techniques such as GRU, standard RNN, and CNN, RNN-LSTM outperforms in capturing long-range dependencies and learning from temporal patterns effectively. The convergence analysis further confirms the robustness of RNN-LSTM, making it the most suitable model for dynamic and complex power system environments. These findings underscore the significance of RNN-based LSTM in enhancing load forecasting reliability, ultimately contributing to improved grid stability, energy management, and operational efficiency. |
| format | Article |
| id | doaj-art-2a1044f73fc543dfaf1df8ea3aea61bc |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
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| series | IEEE Access |
| spelling | doaj-art-2a1044f73fc543dfaf1df8ea3aea61bc2025-08-20T02:06:31ZengIEEEIEEE Access2169-35362025-01-0113990189903110.1109/ACCESS.2025.357630311021601Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart GridsMuhammad Asghar Majeed0https://orcid.org/0009-0007-1034-8413Sotdhipong Phichaisawat1https://orcid.org/0000-0002-6300-2914Furqan Asghar2https://orcid.org/0000-0002-6159-2236Umair Hussan3https://orcid.org/0000-0001-6302-3582Department of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandDepartment of Electrical Engineering, Faculty of Engineering, Chulalongkorn University, Bangkok, ThailandDepartment of Energy Systems Engineering, Faculty of Agricultural Engineering and Technology, University of Agriculture, Faisalabad, PakistanCollege of Mechatronics and Control Engineering, Shenzhen University, Shenzhen, ChinaAccurate load forecasting is essential for ensuring the stability and efficiency of modern power systems, particularly in the context of increasing renewable energy integration. This study proposes an advanced forecasting approach using Recurrent Neural Networks (RNN) with Long Short-Term Memory (LSTM) to enhance electricity demand prediction. The research analyzes key temporal patterns in historical load, renewable energy generation, and weather data to improve forecasting precision. RNN-based LSTM, with its gated memory structure, effectively captures long-term dependencies and mitigates vanishing gradient issues, making it highly suitable for modeling complex time-series data in power systems. Its ability to retain crucial past information across extended sequences enables precise load forecasting, even in the presence of fluctuations and intermittent renewable energy sources. To validate the effectiveness of the proposed technique, the model was implemented and tested using MATLAB/Simulink, ensuring realistic simulation of power system dynamics. Through a comprehensive comparative analysis, RNN-LSTM is found to exhibit superior predictive accuracy, achieving the lowest RMSE (2.2889), MAE (1.1041), and MAPE (1.538%), while also demonstrating faster convergence, lower inference time, and higher stability. Furthermore, LSTM demonstrates superior convergence speed, lower inference time (0.00098 s), and high stability (0.9091), which confirms its robustness. Compared to other deep learning techniques such as GRU, standard RNN, and CNN, RNN-LSTM outperforms in capturing long-range dependencies and learning from temporal patterns effectively. The convergence analysis further confirms the robustness of RNN-LSTM, making it the most suitable model for dynamic and complex power system environments. These findings underscore the significance of RNN-based LSTM in enhancing load forecasting reliability, ultimately contributing to improved grid stability, energy management, and operational efficiency.https://ieeexplore.ieee.org/document/11021601/Recurrent neural networksload forecastingtemporal dependency modelinglong short-term memorygated recurrent unitsconvolutional neural networks |
| spellingShingle | Muhammad Asghar Majeed Sotdhipong Phichaisawat Furqan Asghar Umair Hussan Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids IEEE Access Recurrent neural networks load forecasting temporal dependency modeling long short-term memory gated recurrent units convolutional neural networks |
| title | Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids |
| title_full | Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids |
| title_fullStr | Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids |
| title_full_unstemmed | Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids |
| title_short | Data-Driven Optimized Load Forecasting: An LSTM-Based RNN Approach for Smart Grids |
| title_sort | data driven optimized load forecasting an lstm based rnn approach for smart grids |
| topic | Recurrent neural networks load forecasting temporal dependency modeling long short-term memory gated recurrent units convolutional neural networks |
| url | https://ieeexplore.ieee.org/document/11021601/ |
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