Very short-term residential load forecasting based on deep-autoformer

Very short-term load forecasting (VSLTF) plays an essential role in guaranteeing effective electricity dispatching and generating in residential microgrid systems. However, the extreme fluctuations and irregular data patterns of VSTLF have brought severe challenges to accurate forecasting. Deep learning methods have been mostly utilized in time series predicting tasks like load forecasting. Recently, an Autoformer neural network has been proposed in many time series forecasting scenarios. Based on Autoformer, this paper proposes a new Deep-Autoformer framework, where the extra MLP layers are added to the basic Autoformer framework for a more efficient deep information extraction. Taking a microgrid system in Austin, Texas from the Pecan Street dataset as a case study, Deep-Autoformer and other five baseline models are utilized to forecast the load data of 15 min and one-hour time resolution. The main contributions of the proposed Deep-Autoformer are: (i)the experiment results indicate that the proposed Deep-Autoformer has achieved State-Of-The-Art (SOTA) results in both VSTLF and STLF, and(ii) the ‘deep’ method, where the MLP layers are added in the appropriate positions of the neural network, can contribute to more efficient feature extraction. Moreover, due to the unintuitive phenomenons in the experiment, two hypotheses are also proposed: (i) the long-ago historical data may affect the performance of the auto-correlation mechanism of the Autoformer, and (ii) models are probably overfitting the historical patterns if the time series data are too long. Overall, the proposed Deep-Autoformer can provide a feasible approach and a new baseline for the real application of VSTLF.