Input Parameters Selection in Short-term Load Forecasting Model Based on Incremental Reduction Algorithm
Xiaoxing Zhang · Dianli xitong zidonghua · 2005
A reduction algorithm based on rough set theory is put forward due to various 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. A reduction algorithm through prior heuristic function (RAPHF) algorithm based on attributes-prior algorithm is introduced because reduction algorithm based on dipartite matrix reduction algorithm is a NP problem. On the basis of RAPHF, a rough set incremental algorithm with dynamic mining ability, namely, RAPHF-I is proposed by considering the updating samples. The efficiency and advantage of the proposed method is proved by prediction results of short-term load based on the RAPFF and RAPHF-I.