Research On Short-term Electric Load Forecast Based On Grey Neural Network and Snap-drift Cuckoo Search Algorithm

Abstract

In order to provide users with reliable and qualified power, it becomes an indispensable task to enhance the forecasting capability of the short-term power load. However, the existing approaches of short-term electric load forecasting are not proper enough. A short-term electric load forecasting method based on grey neural network based on snap-drift cuckoo search optimization algorithm(SDCS-GNN) is proposed in this paper. Parameters of gray neural network (GNN) are selected randomly which is similar to the initial spatial position of birds' eggs in the parasitic nest of cuckoo. The SDCS is utilized to search the better weight and threshold of the conventional gray neural network (GNN), which improves the stability and accuracy of the prediction model. To validate the superior performance of the proposed method, several well-known evolutionary algorithms such as particle swarm optimization (PSO), grey wolf optimization(GWO), moth-fire suppression optimization(MFO) and cuckoo search optimization (CS) are employed to constitute the contrast experiment of the prediction of short-term power load. The mean squared error predicted by the SDCS-GNN model is the smallest, which compared with GNN, PSO-GNN, GWO-GNN, MFO-GNN, and CS-GNN is 0.36, 1.79, 15.23, 4.53, 2.93, respectively. The Average prediction accuracy of SDCS-GNN model is better than other models which is 7.1592, 1.427, 15.1516, 11.5438, 10.5202, respectively. The simulation results show that the SDCS-GNN model has better approximation ability and higher prediction accuracy than the conventional GNN and other evolutionary algorithms in the short-term electric load forecasting. The experiments above indicates that the prediction method is effective and feasible.