Yang Li, Yongsheng Ye, Yanlong Xu, Lili Li, Xi Chen, Jianghua Huang
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At the same time, in order to overcome the problem that the prediction model established by the original subsequence has insufficient adaptability in the newly decomposed subsequence, the real-time decomposition strategy is adopted to improve the generalization ability of the model. To further improve the prediction accuracy, an error compensation strategy is constructed in the second stage. The strategy uses adaptive variational mode decomposition (AVMD) to reduce the unpredictability of the error sequence and corrects the initial prediction results based on the temporal convolutional network-gated recurrent unit (TCN-GRU) error compensator. The proposed two-stage forecasting method was evaluated using load data from Queensland, Australia. The analysis results show that the proposed method can better capture the nonlinearity and non-stationarity in the load data. The mean absolute percentage error of its prediction is 0.819%, which are lower than the other compared models, indicating its high applicability in SLTF.</p>","PeriodicalId":49318,"journal":{"name":"Earth Science Informatics","volume":"15 1","pages":""},"PeriodicalIF":2.7000,"publicationDate":"2024-08-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Two-stage forecasting of TCN-GRU short-term load considering error compensation and real-time decomposition\",\"authors\":\"Yang Li, Yongsheng Ye, Yanlong Xu, Lili Li, Xi Chen, Jianghua Huang\",\"doi\":\"10.1007/s12145-024-01456-7\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>With the continuous development of power system and the growth of load demand, accurate short-term load forecasting (SLTF) provides reliable guidance for power system operation and scheduling. Therefore, this paper proposes a two-stage short-term load forecasting method. In the first stage, the original load is processed by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The time series features of the load are extracted by temporal convolutional network (TCN), which is used as an input to realize the initial load prediction based on gated recurrent unit (GRU). At the same time, in order to overcome the problem that the prediction model established by the original subsequence has insufficient adaptability in the newly decomposed subsequence, the real-time decomposition strategy is adopted to improve the generalization ability of the model. To further improve the prediction accuracy, an error compensation strategy is constructed in the second stage. The strategy uses adaptive variational mode decomposition (AVMD) to reduce the unpredictability of the error sequence and corrects the initial prediction results based on the temporal convolutional network-gated recurrent unit (TCN-GRU) error compensator. The proposed two-stage forecasting method was evaluated using load data from Queensland, Australia. The analysis results show that the proposed method can better capture the nonlinearity and non-stationarity in the load data. The mean absolute percentage error of its prediction is 0.819%, which are lower than the other compared models, indicating its high applicability in SLTF.</p>\",\"PeriodicalId\":49318,\"journal\":{\"name\":\"Earth Science Informatics\",\"volume\":\"15 1\",\"pages\":\"\"},\"PeriodicalIF\":2.7000,\"publicationDate\":\"2024-08-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Earth Science Informatics\",\"FirstCategoryId\":\"89\",\"ListUrlMain\":\"https://doi.org/10.1007/s12145-024-01456-7\",\"RegionNum\":4,\"RegionCategory\":\"地球科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Earth Science Informatics","FirstCategoryId":"89","ListUrlMain":"https://doi.org/10.1007/s12145-024-01456-7","RegionNum":4,"RegionCategory":"地球科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
Two-stage forecasting of TCN-GRU short-term load considering error compensation and real-time decomposition
With the continuous development of power system and the growth of load demand, accurate short-term load forecasting (SLTF) provides reliable guidance for power system operation and scheduling. Therefore, this paper proposes a two-stage short-term load forecasting method. In the first stage, the original load is processed by improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN). The time series features of the load are extracted by temporal convolutional network (TCN), which is used as an input to realize the initial load prediction based on gated recurrent unit (GRU). At the same time, in order to overcome the problem that the prediction model established by the original subsequence has insufficient adaptability in the newly decomposed subsequence, the real-time decomposition strategy is adopted to improve the generalization ability of the model. To further improve the prediction accuracy, an error compensation strategy is constructed in the second stage. The strategy uses adaptive variational mode decomposition (AVMD) to reduce the unpredictability of the error sequence and corrects the initial prediction results based on the temporal convolutional network-gated recurrent unit (TCN-GRU) error compensator. The proposed two-stage forecasting method was evaluated using load data from Queensland, Australia. The analysis results show that the proposed method can better capture the nonlinearity and non-stationarity in the load data. The mean absolute percentage error of its prediction is 0.819%, which are lower than the other compared models, indicating its high applicability in SLTF.
期刊介绍:
The Earth Science Informatics [ESIN] journal aims at rapid publication of high-quality, current, cutting-edge, and provocative scientific work in the area of Earth Science Informatics as it relates to Earth systems science and space science. This includes articles on the application of formal and computational methods, computational Earth science, spatial and temporal analyses, and all aspects of computer applications to the acquisition, storage, processing, interchange, and visualization of data and information about the materials, properties, processes, features, and phenomena that occur at all scales and locations in the Earth system’s five components (atmosphere, hydrosphere, geosphere, biosphere, cryosphere) and in space (see "About this journal" for more detail). The quarterly journal publishes research, methodology, and software articles, as well as editorials, comments, and book and software reviews. Review articles of relevant findings, topics, and methodologies are also considered.