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With the deterioration of primary energy market supply, it is important to optimize the raw material buying and dispatching. The annual electric power consumption is one of the most important decision making basis to realize this. Because of the characters of observations, OLS method and neural network model are all not suit for this. PLS extract variables one by one from few historical data. Under...
After China electric power industry market reform, electric power generation groups has competed for exploiting electric generation natural resources. With the consumption of good investment projects, generation groups will put more and more importance on investment risk. This paper adopts Lagrange multiplier to solve the problem of portfolio quadratic programming and further brings forward power...
This paper aims for developing a method, based on rough set (RS) reduction and wavelet neural network (WNN), to improve the efficiency of short-term load forecasting (STLF). The RS reduction could erase redundant characters and this makes it possible to take many influential factors of power load into account, although the learning ability of neural network is limited. Furthermore, WNN is brought...
The net day load curve forecasting plays an important role for electric power system operation. Because of affecting by many factors, daily curve is composed by many regular wave trends and stochastic ones. This makes the poor efficiency and generalization capacity of neural network adopted in forecasting. By using discrete wavelet transform, the complicated load curve could be extracted to many simplex...
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