Future load trends is forecasted based on historical data of power load.By calculating the Hurst parameter value of the power load sequence

it is shown that the vibration intensity sequence meets the obvious long correlation characteristics.Therefore

the long correlation FARIMA model is used to predict the power load sequence.In order to improve the accuracy of prediction

a long correlation FARIMA prediction model based on quantum genetic algorithm optimization is proposed.The quantum genetic algorithm is used to optimize the ARMA（p

q） order of the stationary model after the fractional difference

and the global search is performed according to the appropriate fitness value

so that the optimal order（p

q） of the model can be determined and applied to actual power load value prediction for verification. Experimental results are compared with the results of the traditional AIC（p

q） criteria fixed-stage ARMA model

and the actual power load value data.The comparison results show that the method has higher accuracy of power load value predicting.