Evaluation of an inflow forecast correction method based on Multi-Scenarios division

Abstract

Accurate inflow forecasting is very important for the operation of reservoirs. However, in the actual inflow forecasting, the differences between various parameters and external conditions will affect the forecast results of the model, so it is necessary to correct the forecast results. Most of the existing researches on the correction of forecast results focus on considering the model uncertainty as a whole, and there are few studies on the influence of different external factors on the forecast results. In view of this, the differences of external environment when the forecast occurs and the differences of forecast errors under different forecast scenarios are both considered in this paper. According to the differences in key influencing factors, such as rainfall conditions and foresight periods, the forecast scenarios are divided, and the forecast error distribution law under different scenarios is deduced based on the past forecast error data. On this basis, using Variational Mode Decomposition (VMD) and Long Short-Term Memory (LSTM) Neural Network Model, a new multi-dimensional and multi-attribute inflow forecast correction method considering forecast errors and forecast scenarios is established. This method breaks through the single-dimensional, single-attribute time series limitations of the traditional inflow forecast correction methods. Through the case study of Three Gorges Reservoir (TGR), it was found that compared with the actual situation, the average relative error of the inflow forecast was reduced from the actual 8.32% to 6.36%, a decrease of 1.96%, and the reduction rate reached 23.6%. In addition, other indexes such as mean absolute error, root mean square error and Nash-Sutcliffe efficiency coefficient have all been improved to varying degrees. It showed that the proposed method in this paper can simultaneously consider both the forecast error information of the previous periods and its corresponding forecast scenario information, and increase the effective information input of the correction model to improve the accuracy of the inflow forecast model.