关键词: 关键词: 煤矿负荷预测, 非平稳时间序列, 异常用电识别, 特征融合, 自适应多尺度EMA

Abstract: Abstract: Load sequences in coal mines simultaneously contain long-term trends, periodic patterns, and short-term disturbances, making unified representation difficult for existing forecasting models and resulting in unstable prediction performance during operating-condition transitions. To address this issue, a forecasting framework based on adaptive multi-scale exponential moving averages and multi-path signal feature fusion (MF-AMES) was developed. Discrete wavelet decomposition was first used to extract trend and disturbance components at different scales, while operating-condition labels derived from clustering, daily and weekly time encoding, and multiple exponential moving average sequences were incorporated to construct multi-path structural representations that jointly characterize trend, periodicity, and disturbance information. A two-layer long short-term memory network was then applied to hierarchically capture long-term dependence and local dynamics. An adaptive multi-scale EMA fusion module (AMES) was finally introduced at the output stage, where fusion weights of different smoothing scales were dynamically adjusted according to local fluctuation intensity, thereby improving trend stability and disturbance responsiveness. Experiments on real coal-mine load data from Yunnan Province showed that, compared with the long-horizon forecasting model Patch-TST, MF-AMES reduced the mean absolute error from 47.52 to 32.95, decreased the root mean square error from 65.54 to 45.84, and increased the coefficient of determination (R2) from 0.8860 to 0.9442. These results indicate that MF-AMES maintains both forecasting accuracy and stability under strongly non-stationary and multi-component conditions, providing reliable support for abnormal electricity-use identification and safety-oriented coal-mining operation management.

Key words: Abstract: coal-mine load forecasting, non-stationary time series, abnormal electricity-use identification, feature fusion, adaptive multi-scale EMA.