Scenario prediction for power loads using a pixel convolutional neural network and an optimization strategy

Accurate and reliable prediction of power load is critical to ensure the economy and stability of power systems. However, deterministic point prediction can scarcely be accurate due to the fluctuating and stochastic behavior of power load series, resulting in high risks for the system operation. Scenario prediction is a widely used method to model stochastic behavior by generating a group of possible power load scenarios rather than deterministic point predictions, so that system operators can account for the uncertainty of power loads. In this paper, a new deep generative network-based method is proposed for scenario prediction of power loads, in which structure and parameters are redesigned on the original pixel convolutional neural network (PixelCNN). An optimization model is presented to search for a range of power load scenarios with similar shapes, temporal dependency, and probability distribution as the real ones. Numerical simulations on a real-world power load dataset show that the PixelCNN outperforms other generative networks for the scenario prediction of power loads.