Improved quantile convolutional neural network with two-stage training for daily-ahead probabilistic forecasting of photovoltaic power

Abstract

Probabilistic forecasting is significant in coping with the strong uncertainty of photovoltaic (PV) power, which provides the occurrence scope and corresponding probability information of PV power for the decision of power systems. Convolutional neural networks (CNNs), one of the most advanced and widely used deep-learning methods, are regarded as a promising method for predicting PV power. This paper proposes a daily-ahead probabilistic PV power forecasting method based on an improved quantile CNN (QCNN). First, the QCNN constructs a feature extraction network based on CNNs to mine the deep features of the PV power influence factors. Thereafter, quantile regression (QR) is employed to generate the PV power probability distribution based on the extracted features. However, QR produces non-differentiable loss functions that significantly hinder the training of QCNNs. To address this problem, a two-stage training strategy is proposed in which a CNN is trained using a deterministic forecasting method, and QR is trained using a linear-programming method. This strategy ensures that the training of QCNNs can avoid the effects of the non-differentiable loss functions such that the QCNN can play a complete role in the prediction. The case of a PV plant in Australia demonstrates that QCNNs produce a considerably superior effect when compared to the quantile extreme learning machine, quantile echo state network, direct QR, and radial basis function neural network. The proposed method can be beneficial in terms of decision-making in power systems.