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 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.