Gray Neural Network Forecasting Model of Power Load Based on Ant Colony Algorithm Method

Abstract

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 basis of combination weight making, so as to achieve the goal of optimizing the whole forecasting precision and find the combination weight that can exhibit the high consistency and high precision for the series values, finally the whole forecasting accuracy can be improved obviously. Through the calculation of the power loads in a province which is compared with other algorithms, the results prove that this method can effectively improve the accuracy of power load forecasting.