Power Signal Forecasting by Neural Model with Different Layer Structures

In this paper, the non-stationary power load forecasting by using neural model with different layer structures is presented. In the neural forecasting model we developed, the neuron types used in different layers are different. Each layer is composed of the same kind of neurons. A reliable and accurate neural forecasting model for the non-stationary power loads is trying to be found in this study. To demonstrate the superiority of the model we created, all simulations are executed by using the conventional neural model with same neurons as a comparison. From the results shown, it is clearly found that the neural model we constructed do have better nonlinear mapping and forecasting capabilities in comparison with the conventional neural model