Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting
Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-08-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/17/6230 |
| _version_ | 1827728207347974144 |
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| author | Yuanhang Qi Haoyu Luo Yuhui Luo Rixu Liao Liwei Ye |
| author_facet | Yuanhang Qi Haoyu Luo Yuhui Luo Rixu Liao Liwei Ye |
| author_sort | Yuanhang Qi |
| collection | DOAJ |
| description | Short-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed to effectively combine the clustering process and prediction process. More specifically, the clustering process adopts the maximum deviation similarity criterion clustering algorithm (MDSC) as the clustering framework. A bee-foraging learning particle swarm optimization is further applied to realize the adaptive optimization of its hyperparameters. The prediction process consists of three parts: (i) a 9-dimensional load feature vector is proposed as the classification feature of SVM to obtain the load similarity cluster of the predicted days; (ii) the same kind of data are used as the training data of long short-term memory network; (iii) the trained network is used to predict the power load curve of the predicted day. Finally, experimental results are presented to show that the proposed scheme achieves an advantage in the prediction accuracy, where the mean absolute percentage error between predicted value and real value is only 8.05% for the first day. |
| first_indexed | 2024-03-10T23:25:02Z |
| format | Article |
| id | doaj.art-c1e412c4239444a0bfe6143317ffeb8d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T23:25:02Z |
| publishDate | 2023-08-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-c1e412c4239444a0bfe6143317ffeb8d2023-11-19T08:05:03ZengMDPI AGEnergies1996-10732023-08-011617623010.3390/en16176230Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load ForecastingYuanhang Qi0Haoyu Luo1Yuhui Luo2Rixu Liao3Liwei Ye4School of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, ChinaSchool of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, ChinaSchool of Automation, Guangdong University of Technology, Guangzhou 510006, ChinaSchool of Accountancy, Guangdong Baiyun University, Guangzhou 510550, ChinaSchool of Computer Science, University of Electronic Science and Technology of China, Zhongshan Institute, Zhongshan 528402, ChinaShort-term load forecasting (STLF) plays an important role in facilitating efficient and reliable operations of power systems and optimizing energy planning in the electricity market. To improve the accuracy of power load prediction, an adaptive clustering long short-term memory network is proposed to effectively combine the clustering process and prediction process. More specifically, the clustering process adopts the maximum deviation similarity criterion clustering algorithm (MDSC) as the clustering framework. A bee-foraging learning particle swarm optimization is further applied to realize the adaptive optimization of its hyperparameters. The prediction process consists of three parts: (i) a 9-dimensional load feature vector is proposed as the classification feature of SVM to obtain the load similarity cluster of the predicted days; (ii) the same kind of data are used as the training data of long short-term memory network; (iii) the trained network is used to predict the power load curve of the predicted day. Finally, experimental results are presented to show that the proposed scheme achieves an advantage in the prediction accuracy, where the mean absolute percentage error between predicted value and real value is only 8.05% for the first day.https://www.mdpi.com/1996-1073/16/17/6230power load forecastingneural networkclustering algorithmlong short-term memory network |
| spellingShingle | Yuanhang Qi Haoyu Luo Yuhui Luo Rixu Liao Liwei Ye Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting Energies power load forecasting neural network clustering algorithm long short-term memory network |
| title | Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting |
| title_full | Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting |
| title_fullStr | Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting |
| title_full_unstemmed | Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting |
| title_short | Adaptive Clustering Long Short-Term Memory Network for Short-Term Power Load Forecasting |
| title_sort | adaptive clustering long short term memory network for short term power load forecasting |
| topic | power load forecasting neural network clustering algorithm long short-term memory network |
| url | https://www.mdpi.com/1996-1073/16/17/6230 |
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