Short-Term Electrical Load Forecasting Based On Improved BP Neural Network

This thesis carries on to the meaning and actual states of Load forecasting to say all first, analyzing the characteristic of load and Load forecasting, tallying up composition and sorts and load periodic change regulations of load, analyzing every kind of factor of load of impact. And consider every kind of impact, apply improved BP neural network, create short-team load forecasting models. At input load, the type of date and every kind of weather factors that considered the close by date in the variable, enter to unify the transaction to the input quantity, to the temperature, rain to shine on etc. the factor to put forward the special disposal with optics. The training of the network applies the SCG calculate way, putting up the velocity and forecasting accuracies of the neural network on the very big degree.