Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models
Short-Term Load Forecasting (STLF) is essential for the efficient management of power systems, as it improves forecasting accuracy while optimizing power scheduling efficiency. Despite significant recent advancements in STLF models, forecasting accuracy in high-volatility regions remains a key chall...
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MDPI AG
2024-12-01
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| Series: | Applied Sciences |
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| author | Bingbing Tang Jie Hu Mei Yang Chenglong Zhang Qiang Bai |
| author_facet | Bingbing Tang Jie Hu Mei Yang Chenglong Zhang Qiang Bai |
| author_sort | Bingbing Tang |
| collection | DOAJ |
| description | Short-Term Load Forecasting (STLF) is essential for the efficient management of power systems, as it improves forecasting accuracy while optimizing power scheduling efficiency. Despite significant recent advancements in STLF models, forecasting accuracy in high-volatility regions remains a key challenge. To address this issue, this paper introduces a hybrid load forecasting model that integrates the Long Short-Term Memory Network (LSTM) with the Stochastic Configuration Network (SCN). We first verify the Universal Approximation Property of SCN through experiments on two regression datasets. Subsequently, we reconstruct the features and input them into the LSTM for feature extraction. These extracted feature vectors are then used as inputs for SCN-based STLF. Finally, we evaluate the performance of the LSTM-SCN model against other baseline models using the Australian Electricity Load dataset. We also select five high-volatility regions in the test set to validate the LSTM-SCN model’s advantages in such scenarios. The results show that the LSTM-SCN model achieved an RMSE of 56.970, MAE of 43.033, and MAPE of 0.492% on the test set. Compared to the next best model, the LSTM-SCN model reduced errors by 6.016, 8.846, and 0.053% for RMSE, MAE, and MAPE, respectively. Additionally, the model consistently outperformed across all five high-volatility regions analyzed. These findings highlight its contribution to improved power system management, particularly in challenging high-volatility scenarios. |
| format | Article |
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| institution | OA Journals |
| issn | 2076-3417 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | MDPI AG |
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| series | Applied Sciences |
| spelling | doaj-art-2e00114698ef42eea486d895a87ebb5d2025-08-20T02:00:54ZengMDPI AGApplied Sciences2076-34172024-12-0114241160610.3390/app142411606Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid ModelsBingbing Tang0Jie Hu1Mei Yang2Chenglong Zhang3Qiang Bai4College of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, ChinaCollege of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, ChinaCollege of Big Data Statistics, Guizhou University of Finance and Economics, Guiyang 550025, ChinaSchool of Data Science, The Chinese University of Hong Kong, Shenzhen 518172, ChinaSchool of Mechanical Engineering, Guiyang University, Guiyang 550002, ChinaShort-Term Load Forecasting (STLF) is essential for the efficient management of power systems, as it improves forecasting accuracy while optimizing power scheduling efficiency. Despite significant recent advancements in STLF models, forecasting accuracy in high-volatility regions remains a key challenge. To address this issue, this paper introduces a hybrid load forecasting model that integrates the Long Short-Term Memory Network (LSTM) with the Stochastic Configuration Network (SCN). We first verify the Universal Approximation Property of SCN through experiments on two regression datasets. Subsequently, we reconstruct the features and input them into the LSTM for feature extraction. These extracted feature vectors are then used as inputs for SCN-based STLF. Finally, we evaluate the performance of the LSTM-SCN model against other baseline models using the Australian Electricity Load dataset. We also select five high-volatility regions in the test set to validate the LSTM-SCN model’s advantages in such scenarios. The results show that the LSTM-SCN model achieved an RMSE of 56.970, MAE of 43.033, and MAPE of 0.492% on the test set. Compared to the next best model, the LSTM-SCN model reduced errors by 6.016, 8.846, and 0.053% for RMSE, MAE, and MAPE, respectively. Additionally, the model consistently outperformed across all five high-volatility regions analyzed. These findings highlight its contribution to improved power system management, particularly in challenging high-volatility scenarios.https://www.mdpi.com/2076-3417/14/24/11606Short-Term Load Forecastinghigh-volatility regionsLong Short-Term MemoryStochastic Configuration Networkuniversal approximation property |
| spellingShingle | Bingbing Tang Jie Hu Mei Yang Chenglong Zhang Qiang Bai Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models Applied Sciences Short-Term Load Forecasting high-volatility regions Long Short-Term Memory Stochastic Configuration Network universal approximation property |
| title | Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models |
| title_full | Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models |
| title_fullStr | Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models |
| title_full_unstemmed | Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models |
| title_short | Enhancing Short-Term Load Forecasting Accuracy in High-Volatility Regions Using LSTM-SCN Hybrid Models |
| title_sort | enhancing short term load forecasting accuracy in high volatility regions using lstm scn hybrid models |
| topic | Short-Term Load Forecasting high-volatility regions Long Short-Term Memory Stochastic Configuration Network universal approximation property |
| url | https://www.mdpi.com/2076-3417/14/24/11606 |
| work_keys_str_mv | AT bingbingtang enhancingshorttermloadforecastingaccuracyinhighvolatilityregionsusinglstmscnhybridmodels AT jiehu enhancingshorttermloadforecastingaccuracyinhighvolatilityregionsusinglstmscnhybridmodels AT meiyang enhancingshorttermloadforecastingaccuracyinhighvolatilityregionsusinglstmscnhybridmodels AT chenglongzhang enhancingshorttermloadforecastingaccuracyinhighvolatilityregionsusinglstmscnhybridmodels AT qiangbai enhancingshorttermloadforecastingaccuracyinhighvolatilityregionsusinglstmscnhybridmodels |