Investigation of Forecast Accuracy and its Impact on the Efficiency of Data-Driven Forecast-Based Reservoir Operating Rules

Today, variable flow pattern, which uses static rule curves, is considered one of the challenges reservoir operation. One way to overcome this problem develop forecast-based curves. However, managers must have an estimate influence forecast accuracy on operation performance due intrinsic limitations models. This study attempts a model and investigate effects corresponding two conventional To model, methods according autocorrelation wrapper-based feature selection models are introduced deal with wavelet components inflow. Finally, performances polynomial hedging curves investigated using forecasted actual inflows. The results applying Dez in Iran visualized that 4% improvement correlation coefficient coupled could reduce relative deficit curve by 8.1%. Moreover, 2% 10% Willmott Nash—Sutcliffe indices, same 8.1% reduction can be expected. Similar observed for rules where increasing decreased 15.5%. In general, it was concluded more sensitive than are.