traffic prediction

load forecasting

International Journal of Electrical Power & Energy Systems

• A probabilistic forecasting method with interpretability and low temporal resolution for mid-term hourly load time series is proposed. • Model complexity is reduced by reconstructing raw univariate load time series to preserve the day-to-day load differences while shortening the time series. • It can explain how much each input feature influences the load at different times of the day. • A novel quantile loss function with quantile constraints and PI penalty significantly improves the accuracy of ITFT by increasing the reliability and sharpness of PIs. The growth of distributed renewable energy and demand-side responsiveness has increased the difficulty of mid-term hourly load time-series forecasting. This study presents a probabilistic forecasting method for hourly load time series based on an improved temporal fusion transformer (ITFT) model to achieve more accurate and thorough forecasting results. The raw univariate time series of the hourly load was reconstructed into multiple day-to-day load time series at different hour-points to reconcile the contradiction between learning the temporal dependence on a long prediction horizon and reducing model complexity. The corresponding hour point was used as a static covariable input to distinguish the differences. Based on the original temporal fusion transformer (TFT) model, the ITFT model replaces the long short-term memory (LSTM) with a gated recurrent unit (GRU) to learn long-term dependence more efficiently. Furthermore, quantile constraints and prediction interval (PI) penalty terms were incorporated into the original quantile loss function to prevent quantile crossover and construct more compact prediction intervals (PIs). The results of two actual examples show that the proposed method is explanatory and can significantly improve the reliability and compactness of probabilistic load forecasting (PLF) results compared with other popular methods.

Probabilistic forecasting method for mid-term hourly load time series based on an improved temporal fusion transformer model