Based on BP neural network power system
Short-term load forecasting
Abstract
Short-term load forecasting in real-time power system control and to ensure
economic, safe and reliable operation plays an important role, it has become a modern
power system energy management system is an important component. Load
forecasting errors will lead to sharp increase in operating and production costs,
therefore, accurately predict the power to become the workers and technical personnel
to address the problem.
Various power system affected by the load change, on the one hand, there is the
unknown load change caused by fluctuations in uncertainty; the other hand, there are
periodic changes in the laws, which makes a similar load curve. At the same time, due
to weather, holidays and other special circumstances of, and differences in the load
changes occur, showing a strong nonlinearity.
In this paper, BP neural network based prediction method, the biggest advantage
of this approach is that the nonlinear characteristics of a large number of
non-accuracy of the law of adaptive function
.
In this paper, BP neural network for
short term load forecasting in power system to do further research and design BP
neural network through the MATLAB , simulation results show that BP neural
network in the short-term load forecasting is feasible, and can better reflect the load
predict the nonlinear characteristics, but because this article does not consider the
climate, temperature, holidays and other factors, the data, so it is not very satisfactory
simulation results, but still certainly better than the traditional prediction ,and it is a
need to be us to new areas of research and development.
Keywords
：
Power Systems; BP neural network; Short-term Load forecasting;
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