基于相似日聚类-TCN-Informer组合模型的短期电力负荷预测研究OA

[Objective]Existing short-term power load forecasting models struggle to simultaneously learn the inherent differences in electricity consumption patterns across diverse scenarios and effectively capture long-range temporal dependencies,so their forecasting accuracy is significantly restricted in high-volatility situations such as extreme weather events,peak load periods,or sudden changes in user behavior.To address this limitation,this paper proposes a novel hybrid forecasting framework named"similar-day clustering-temporal convolutional network(TCN)-Informer".[Methods]First,the fuzzy C-means(FCM)clustering algorithm is employed to categorize historical load samples into three distinct similar-day clusters(working days,weekends,and holidays)based on historical load data and calendar features(e.g.,weekday/weekend flags,holiday labels).This preprocessing step effectively mitigates cross-pattern noise interference caused by heterogeneous consumption behaviors,laying a solid foundation for subsequent feature extraction.Second,for each clustered dataset,a causal dilated TCN is utilized to extract local temporal features and capture short-to medium-term load fluctuations,as its dilated convolution structure enables efficient expansion of the receptive field without increasing computational complexity while avoiding future information leakage.Subsequently,the Informer model equipped with a probabilistic sparse attention mechanism is introduced to capture long-range dependencies in load time series,which significantly reduces the computational burden compared to traditional attention mechanisms and enhances the model's ability to learn long-term temporal correlations.Moreover,exogenous variables closely related to power load,including meteorological factors(temperature,humidity,solar irradiation)and real-time electricity prices,are integrated into the forecasting framework to improve the comprehensiveness of input information.To optimize the model's hyperparameters,Bayesian optimization is adopted to efficiently search the global hyperparameter space,outperforming traditional grid search in both search efficiency and optimization performance.[Results]Experimental validations for 24-hour load forecasting are conducted using 30-minute resolution data from the AEMO-NSW power market,covering a one-year period with over 17 000 samples.The results demonstrate that the proposed model achieves remarkable performance:its root mean square error(RMSE)and mean absolute error(MAE)reduce by 16.59%and 14.65%compared with those of the classic LSTM model,and by 12.33%and 11.60%in contrast to the advanced TimesNet model,with the mean absolute percentage error(MAPE)as low as 3.02%.Particularly in high-volatility scenarios,the model maintains superior stability and accuracy.[Conclusion]These findings fully verify the prominent advantages of the proposed method in forecasting precision and robustness,which can provide reliable decision-making support for power grid dispatching,electricity spot trading,and energy resource optimization in practical power systems.