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This paper proposes a composite method for short-term load forecasting, which is based on fuzzy clustering wavelet decomposition and BP neural network. Firstly, the similar-day's load is selected as the input load based on the fuzzy clustering method; secondly, the wavelet method is applied to decompose the similar-day load into the low frequency and high frequency components, from which the feature...
This paper put forward a new method of the variable structure artificial neural network model for mid-long term load forecasting. We overcome the shortcoming of single train set of ANN. It can be seen from the example this method can improve effectively the forecast accuracy and speed. The forecast model was tested and the result showed that it was an effective way to forecast mid-long term electric...
Support vector machines (SVM) has been used in load forecasting field. The noise and redundancy of sample data are important factors to the generalized performance of SVM. They can cause some disadvantages of slow convergence speed and low forecasting accuracy. A SVM forecasting method for short-term load forecasting based on rough sets (RS-SVM) is developed in this paper, using rough sets algorithm...
Because power loads are influenced by various factors, and the changes of power load presents are complicate, the traditional forecasting methods are always not satisfied. According to the random-increase and non-linearity fluctuation of residual series, gray neural network forecasting can reflect the increase character and non-linearity relationship. This paper using the improved ACO method as the...
In order to establish a high accuracy forecasting model for short-term electric power load, this paper made a change to grey differential equation utilizing the fundamental theorem of discrete time function. Through mapping the parameters of the equation into the BP neural network, giving the corresponding parameters when the sequence sample of load was converged in the network. In this case, optimizing...
Neural network can increase forecasting accuracy of power system load , but canpsilat provide explanation for forecast reason, so this paper proposes a short-period load forecasting method based on structural neural network. The paper respectively set up such models as historical load data forecasting model, weather forecasting model and date type model. First three models are respectively learned...
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