Rough Set Incremental Reduction Algorithm and Its Application in Dynamic Choosing Input Variable of Short Term Load Forecasting Model

In this paper a reduction algorithm based on rough set theory is presented due to too much factors that influence accuracy in the power load forecasting. The reduction algorithm introduced to mine more correlative attributes in the pending forecasting components, ensures not only the rationality of input parameters of forecasting model but also the selection of input parameters of ANN model. An RAPHF (reduction algorithm through prior heuristic function) algorithm based on attributes-prior algorithm is introduced because reduction Algorithm based on dipartite matrix reduction algorithm is a NP-hard problem. On the basis of RAPHF, a rough set incremental algorithm with dynamic mining ability, namely, RAPHF-I is proposed when considering the updating samples. The efficiency and advantage of our method is proved by prediction results of short-term load based on the RAPFF and RAPHF-I.