HWDQT: A hybrid quantum machine learning method for ultra-short-term distributed photovoltaic power prediction

This paper proposes a novel framework for ultra-short-term distributed photovoltaic (PV) power prediction, aiming to improve prediction accuracy and reliability, ensuring the safe, stable, and economically efficient operation of active distribution networks. This framework uniquely integrates data augmentation, clustering, and quantum machine learning (QML). Firstly, considering the problem of insufficient data under extreme weather fluctuation patterns, the Wasserstein Generative Adversarial Network with Gradient Penalty (WGAN-GP) incorporating bidirectional Long Short-Term Memory (BiLSTM) layers is adopted for data expansion. Introducing BiLSTM network layers enhances its ability to capture long-term sequence dependencies. Secondly, a two-stage clustering method is specifically designed to classify weather fluctuation patterns accurately. On this basis, a hybrid quantum–classical prediction model is constructed by combining Variational Quantum Circuits (VQC) with Long Short-Term Memory (LSTM) networks to compensate for the shortcomings of traditional methods in feature mining. In addition, this article introduces a new evaluation metric: the Improved Weighted Mean Absolute Percentage Error (WMAPE-$\beta$), which is used to measure model performance more comprehensively. The comparative experiments indicate that the proposed model outperforms BiLSTM, LSTM, DLinear, Gated Recurrent Unit (GRU), Convolutional Neural Network-Gated Recurrent Unit (CNN-GRU), and Temporal Convolutional Network (TCN) models in terms of prediction accuracy, convergence speed, stability, and generalization capability. Under different weather fluctuation patterns, the average $R^2$ values of the proposed model are 0.998, 0.993, and 0.984, respectively. This study provides a new reference direction for accurate prediction of distributed PV power, which is of great significance for optimizing grid integration and energy management in renewable energy systems.