Power Engineering Investment Forecasting Based on Covering Rough Set

摘要

Investment forecasting is an important issue for power engineering's plan, investment and operation. In this paper, a new investment forecasting model is proposed based on covering rough set combined with neighborhood classifier theory. The classifier is used to classify the related historical data resulting in testing knowledge bases with certain testing precision. The knowledge base with the highest testing precision is chose to forecast the investment of new power engineering. The experiment results point out that the proposed model can forecast the power engineering investment effectually.