Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction
Accurate electricity consumption forecasting is essential for power scheduling. In short-term forecasting, electricity consumption data exhibit periodic patterns, as well as fluctuations associated with production events. Traditional forecasting methods typically focus on sequential features of the...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/23/5813 |
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| _version_ | 1846124328793407488 |
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| author | Sheng Huang Huakun Que Lukun Zeng Jingxu Yang Kaihong Zheng |
| author_facet | Sheng Huang Huakun Que Lukun Zeng Jingxu Yang Kaihong Zheng |
| author_sort | Sheng Huang |
| collection | DOAJ |
| description | Accurate electricity consumption forecasting is essential for power scheduling. In short-term forecasting, electricity consumption data exhibit periodic patterns, as well as fluctuations associated with production events. Traditional forecasting methods typically focus on sequential features of the data, which may lead to an over-smoothing issue for the fluctuations. In practice, the fluctuations of electricity consumption associated with these events tend to follow recognizable patterns. By emphasizing the impact of these experiential electricity consumption fluctuations on the current prediction process, we can capture the volatility variations to alleviate the over-smoothing problem. To this end, we propose an encoding decomposition-based multi-scale graph neural network (CMNN). The CMNN starts by decomposing the electricity data into various components. For the high-order components that exhibit approximate periodic behavior, the CMNN designs a Multi-scale Bi-directional Long Short-Term Memory (MBLSTM) network for fitting and prediction. For the low-order components that exhibit fluctuations, the CMNN transforms these components from one-dimensional time series into a two-dimensional low-order component graph to model the volatility of the low-order components, and proposes a Gaussian Graph Auto-Encoder to forecast the low-order components. Finally, the CMNN combines the predicted components to produce the final electricity consumption prediction. Experiments demonstrate that the CMNN enhances the accuracy of electricity consumption predictions. |
| format | Article |
| id | doaj-art-f4b8b2e11bcc4f8da67ad613bb27367b |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-f4b8b2e11bcc4f8da67ad613bb27367b2024-12-13T16:24:59ZengMDPI AGEnergies1996-10732024-11-011723581310.3390/en17235813Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption PredictionSheng Huang0Huakun Que1Lukun Zeng2Jingxu Yang3Kaihong Zheng4Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, ChinaMetrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, ChinaDigital Grid Group Co., Ltd., China Southern Power Grid, Guangzhou 510663, ChinaDigital Grid Group Co., Ltd., China Southern Power Grid, Guangzhou 510663, ChinaDigital Grid Group Co., Ltd., China Southern Power Grid, Guangzhou 510663, ChinaAccurate electricity consumption forecasting is essential for power scheduling. In short-term forecasting, electricity consumption data exhibit periodic patterns, as well as fluctuations associated with production events. Traditional forecasting methods typically focus on sequential features of the data, which may lead to an over-smoothing issue for the fluctuations. In practice, the fluctuations of electricity consumption associated with these events tend to follow recognizable patterns. By emphasizing the impact of these experiential electricity consumption fluctuations on the current prediction process, we can capture the volatility variations to alleviate the over-smoothing problem. To this end, we propose an encoding decomposition-based multi-scale graph neural network (CMNN). The CMNN starts by decomposing the electricity data into various components. For the high-order components that exhibit approximate periodic behavior, the CMNN designs a Multi-scale Bi-directional Long Short-Term Memory (MBLSTM) network for fitting and prediction. For the low-order components that exhibit fluctuations, the CMNN transforms these components from one-dimensional time series into a two-dimensional low-order component graph to model the volatility of the low-order components, and proposes a Gaussian Graph Auto-Encoder to forecast the low-order components. Finally, the CMNN combines the predicted components to produce the final electricity consumption prediction. Experiments demonstrate that the CMNN enhances the accuracy of electricity consumption predictions.https://www.mdpi.com/1996-1073/17/23/5813graph attention networkmulti-scale graph neural networklow-order component graphelectricity consumption prediction |
| spellingShingle | Sheng Huang Huakun Que Lukun Zeng Jingxu Yang Kaihong Zheng Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction Energies graph attention network multi-scale graph neural network low-order component graph electricity consumption prediction |
| title | Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction |
| title_full | Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction |
| title_fullStr | Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction |
| title_full_unstemmed | Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction |
| title_short | Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction |
| title_sort | multi scale graph attention network based on encoding decomposition for electricity consumption prediction |
| topic | graph attention network multi-scale graph neural network low-order component graph electricity consumption prediction |
| url | https://www.mdpi.com/1996-1073/17/23/5813 |
| work_keys_str_mv | AT shenghuang multiscalegraphattentionnetworkbasedonencodingdecompositionforelectricityconsumptionprediction AT huakunque multiscalegraphattentionnetworkbasedonencodingdecompositionforelectricityconsumptionprediction AT lukunzeng multiscalegraphattentionnetworkbasedonencodingdecompositionforelectricityconsumptionprediction AT jingxuyang multiscalegraphattentionnetworkbasedonencodingdecompositionforelectricityconsumptionprediction AT kaihongzheng multiscalegraphattentionnetworkbasedonencodingdecompositionforelectricityconsumptionprediction |