Multi-Scale Graph Attention Network Based on Encoding Decomposition for Electricity Consumption Prediction
Sheng Huang,
Huakun Que,
Lukun Zeng,
Jingxu Yang and
Kaihong Zheng (zheng_kh@163.com)
Additional contact information
Sheng Huang: Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Huakun Que: Metrology Center of Guangdong Power Grid Corporation, Guangzhou 510080, China
Lukun Zeng: Digital Grid Group Co., Ltd., China Southern Power Grid, Guangzhou 510663, China
Jingxu Yang: Digital Grid Group Co., Ltd., China Southern Power Grid, Guangzhou 510663, China
Kaihong Zheng: Digital Grid Group Co., Ltd., China Southern Power Grid, Guangzhou 510663, China
Energies, 2024, vol. 17, issue 23, 1-18
Abstract:
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.
Keywords: graph attention network; multi-scale graph neural network; low-order component graph; electricity consumption prediction (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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