Abstract
Wind power forecasting, which is necessary for wind farm, is significant to the dispatch of power grid since the characteristics of wind change intermittently. In this paper, a hybrid model for short-term wind power forecasting based on MIV, Tversky model and GA-BP neural network is formulated. The Mean Impact Value (MIV) method is used to monitor the input variable of BP neural network which will simplify the neural network model and reduce the training time. And the Tversky model is used for cluster analysis, which keeps watch over the similar training set of BP neural network. In addition, the Genetic Algorithm (GA) is used to optimize the initial weights and thresholds of BP neural network to achieve the global optimization. Simulation results show that the method combined with MIV, Tversky and GA-BP can improve the accuracy of short-term wind power forecasting.
Highlights
The output power of the wind farm fluctuate frequently, bringing great difference to grid-connection [1]
We argue a short-term wind power forecasting method based on Mean Impact Value (MIV), Tversky model and Genetic Algorithm (GA)-Back Propagation (BP) neural network
We take the two new sample set as the simulation sample set and use the trained model to calculate the output value of the samples and the subtraction, which is the Impact Value (IV) of influence of the changing argument, and the mean value of the IV of each variable is Mean Impact Value (MIV)
Summary
The output power of the wind farm fluctuate frequently, bringing great difference to grid-connection [1]. The support vector machine method transfers the non-linearly unseparable samples in low dimensional space into linearly separable one in high dimensional space via kernel function, which can greatly avoid local optimum. This method don’t make enough analysis of input variables so the accuracy is not high. We argue a short-term wind power forecasting method based on MIV, Tversky model and GA-BP neural network. The Genetic Algorithm (GA) optimizes the initial weights and thresholds of Back Propagation (BP) neural network to achieve the global optimization [12] This method can optimize the input variables to improve the accuracy of short-term wind power forecasting
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