Comparative Analysis of Deep Learning Techniques for Load Forecasting in Power Systems Using Single-Layer and Hybrid Models
Accurate power load forecasting is critical to maintaining the stability and efficiency of power systems. However, due to the complex and fluctuating nature of power load patterns, physical calculations are often inefficient and time-consuming. In addition, traditional methods, known as statistical...
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| Format: | Article |
| Language: | English |
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Wiley
2024-01-01
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| Series: | International Transactions on Electrical Energy Systems |
| Online Access: | http://dx.doi.org/10.1155/2024/5587728 |
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| author | Jiyeon Jang Beopsoo Kim Insu Kim |
| author_facet | Jiyeon Jang Beopsoo Kim Insu Kim |
| author_sort | Jiyeon Jang |
| collection | DOAJ |
| description | Accurate power load forecasting is critical to maintaining the stability and efficiency of power systems. However, due to the complex and fluctuating nature of power load patterns, physical calculations are often inefficient and time-consuming. In addition, traditional methods, known as statistical learning methods, require not only mathematical background and understanding but also statistical background and understanding. To overcome these difficulties, the authors proposed a simpler way to predict load by using artificial intelligence. This study investigated the performance of forecasting techniques, including three single-layer and seven hybrid multilayer deep learning models that combine them. This study also analyzed the effect of hyperparameters on the learning results by varying the epoch and activation functions. To evaluate and analyze the performance of the deep learning model, this study used load data from the power system in Jeju Island, Korea. In addition, this study included weather factors that may affect the load to improve the prediction performance. The prediction process is performed on the Python platform, and the model that showed the highest accuracy was LSTM-CNN, a hybrid combination of LSTM and CNN models. Considering both the maximum and minimum error, the error value was low at 0.231%. By providing detailed insights into the entire research process, including data collection, preprocessing, scaling, prediction, and analysis, this study provided valuable guidance for future research in this area. |
| format | Article |
| id | doaj-art-fe4d592a25ab4daeb18af580fc9f60d0 |
| institution | Kabale University |
| issn | 2050-7038 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Transactions on Electrical Energy Systems |
| spelling | doaj-art-fe4d592a25ab4daeb18af580fc9f60d02025-02-03T07:23:38ZengWileyInternational Transactions on Electrical Energy Systems2050-70382024-01-01202410.1155/2024/5587728Comparative Analysis of Deep Learning Techniques for Load Forecasting in Power Systems Using Single-Layer and Hybrid ModelsJiyeon Jang0Beopsoo Kim1Insu Kim2Electrical and Computer EngineeringElectrical and Computer EngineeringElectrical and Computer EngineeringAccurate power load forecasting is critical to maintaining the stability and efficiency of power systems. However, due to the complex and fluctuating nature of power load patterns, physical calculations are often inefficient and time-consuming. In addition, traditional methods, known as statistical learning methods, require not only mathematical background and understanding but also statistical background and understanding. To overcome these difficulties, the authors proposed a simpler way to predict load by using artificial intelligence. This study investigated the performance of forecasting techniques, including three single-layer and seven hybrid multilayer deep learning models that combine them. This study also analyzed the effect of hyperparameters on the learning results by varying the epoch and activation functions. To evaluate and analyze the performance of the deep learning model, this study used load data from the power system in Jeju Island, Korea. In addition, this study included weather factors that may affect the load to improve the prediction performance. The prediction process is performed on the Python platform, and the model that showed the highest accuracy was LSTM-CNN, a hybrid combination of LSTM and CNN models. Considering both the maximum and minimum error, the error value was low at 0.231%. By providing detailed insights into the entire research process, including data collection, preprocessing, scaling, prediction, and analysis, this study provided valuable guidance for future research in this area.http://dx.doi.org/10.1155/2024/5587728 |
| spellingShingle | Jiyeon Jang Beopsoo Kim Insu Kim Comparative Analysis of Deep Learning Techniques for Load Forecasting in Power Systems Using Single-Layer and Hybrid Models International Transactions on Electrical Energy Systems |
| title | Comparative Analysis of Deep Learning Techniques for Load Forecasting in Power Systems Using Single-Layer and Hybrid Models |
| title_full | Comparative Analysis of Deep Learning Techniques for Load Forecasting in Power Systems Using Single-Layer and Hybrid Models |
| title_fullStr | Comparative Analysis of Deep Learning Techniques for Load Forecasting in Power Systems Using Single-Layer and Hybrid Models |
| title_full_unstemmed | Comparative Analysis of Deep Learning Techniques for Load Forecasting in Power Systems Using Single-Layer and Hybrid Models |
| title_short | Comparative Analysis of Deep Learning Techniques for Load Forecasting in Power Systems Using Single-Layer and Hybrid Models |
| title_sort | comparative analysis of deep learning techniques for load forecasting in power systems using single layer and hybrid models |
| url | http://dx.doi.org/10.1155/2024/5587728 |
| work_keys_str_mv | AT jiyeonjang comparativeanalysisofdeeplearningtechniquesforloadforecastinginpowersystemsusingsinglelayerandhybridmodels AT beopsookim comparativeanalysisofdeeplearningtechniquesforloadforecastinginpowersystemsusingsinglelayerandhybridmodels AT insukim comparativeanalysisofdeeplearningtechniquesforloadforecastinginpowersystemsusingsinglelayerandhybridmodels |