Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series
In recent years, deep learning technology has demonstrated remarkable potential across various prediction tasks. However, existing deep learning models still fall short in fully exploiting the periodicity, trends, and residual characteristics inherent in energy consumption data. To address these def...
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| Format: | Article |
| Language: | zho |
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Editorial Office of Control and Information Technology
2025-06-01
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| Series: | Kongzhi Yu Xinxi Jishu |
| Subjects: | |
| Online Access: | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.03.007 |
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| author | CHEN Bowen DENG Jian ZHU Qianliu |
| author_facet | CHEN Bowen DENG Jian ZHU Qianliu |
| author_sort | CHEN Bowen |
| collection | DOAJ |
| description | In recent years, deep learning technology has demonstrated remarkable potential across various prediction tasks. However, existing deep learning models still fall short in fully exploiting the periodicity, trends, and residual characteristics inherent in energy consumption data. To address these deficiencies, this paper proposes a novel prediction model called Periodformer. The model begins by decomposing time series into three components: trend, period, and residual. Each component is modeled separately, and the prediction results from these models are then integrated, leading to significantly improved prediction accuracy. Experimental results showed that Periodformer achieved reductions in both Mean Absolute Error (MAE) and Mean Squared Error (MSE) of 5.56% and 11.85%, respectively, compared to the existing Transformer model, while exhibiting strong robustness against data noise. |
| format | Article |
| id | doaj-art-9c95090d2b504b46b9b005dd3ea590c0 |
| institution | Kabale University |
| issn | 2096-5427 |
| language | zho |
| publishDate | 2025-06-01 |
| publisher | Editorial Office of Control and Information Technology |
| record_format | Article |
| series | Kongzhi Yu Xinxi Jishu |
| spelling | doaj-art-9c95090d2b504b46b9b005dd3ea590c02025-08-25T06:57:42ZzhoEditorial Office of Control and Information TechnologyKongzhi Yu Xinxi Jishu2096-54272025-06-015460117840777Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time SeriesCHEN BowenDENG JianZHU QianliuIn recent years, deep learning technology has demonstrated remarkable potential across various prediction tasks. However, existing deep learning models still fall short in fully exploiting the periodicity, trends, and residual characteristics inherent in energy consumption data. To address these deficiencies, this paper proposes a novel prediction model called Periodformer. The model begins by decomposing time series into three components: trend, period, and residual. Each component is modeled separately, and the prediction results from these models are then integrated, leading to significantly improved prediction accuracy. Experimental results showed that Periodformer achieved reductions in both Mean Absolute Error (MAE) and Mean Squared Error (MSE) of 5.56% and 11.85%, respectively, compared to the existing Transformer model, while exhibiting strong robustness against data noise.http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.03.007train energy consumption predictiontime series decompositionTransformerPeriodformerdeep learning models |
| spellingShingle | CHEN Bowen DENG Jian ZHU Qianliu Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series Kongzhi Yu Xinxi Jishu train energy consumption prediction time series decomposition Transformer Periodformer deep learning models |
| title | Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series |
| title_full | Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series |
| title_fullStr | Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series |
| title_full_unstemmed | Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series |
| title_short | Periodformer: An Energy Consumption Prediction Model Based on Decomposition of Time Series |
| title_sort | periodformer an energy consumption prediction model based on decomposition of time series |
| topic | train energy consumption prediction time series decomposition Transformer Periodformer deep learning models |
| url | http://ctet.csrzic.com/thesisDetails#10.13889/j.issn.2096-5427.2025.03.007 |
| work_keys_str_mv | AT chenbowen periodformeranenergyconsumptionpredictionmodelbasedondecompositionoftimeseries AT dengjian periodformeranenergyconsumptionpredictionmodelbasedondecompositionoftimeseries AT zhuqianliu periodformeranenergyconsumptionpredictionmodelbasedondecompositionoftimeseries |