Wrapper Feature Selection Significantly Improves Nonlinear Prediction of Electricity Spot Prices

摘要

The paper describes the selection of input delays for Focused Time Delay Neural Network (FTDNN). The problem is understood as a feature subset selection problem, where one looks for a set of features (input delays) that minimizes the mean absolute percentage error. This combinatorial optimization problem is solved using sequential forward search. First, an application of the prediction method to hourly Ontario electricity price forecasting is presented, demonstrating the importance of the feature selection. Although the network with only one hidden unit was used, the wrapper based feature selection caused that it outperforms all state-of the art approaches considered for comparison.