Quantum Neural Networks for Wind Energy Forecasting: A Comparative Study of Performance and Scalability with Classical Models

TL;DR

This study evaluates quantum neural networks for wind energy forecasting, demonstrating their competitive performance and scalability compared to classical models, with insights into dataset size and circuit complexity effects.

Contribution

It provides a comprehensive comparison of QNN configurations for wind power prediction, highlighting their potential advantages over classical methods.

Findings

QNNs achieve comparable or slightly better accuracy than classical models.

Dataset size and circuit complexity influence QNN performance and simulation time.

QNNs show promise for integration into renewable energy forecasting workflows.

Abstract

Quantum Neural Networks (QNNs), a prominent approach in Quantum Machine Learning (QML), are emerging as a powerful alternative to classical machine learning methods. Recent studies have focused on the applicability of QNNs to various tasks, such as time-series forecasting, prediction, and classification, across a wide range of applications, including cybersecurity and medical imaging. With the increased use of smart grids driven by the integration of renewable energy systems, machine learning plays an important role in predicting power demand and detecting system disturbances. This study provides an in-depth investigation of QNNs for predicting the power output of a wind turbine. We assess the predictive performance and simulation time of six QNN configurations that are based on the Z Feature Map for data encoding and varying ansatz structures. Through detailed cross-validation experiments and tests on an unseen hold-out dataset, we experimentally demonstrate that QNNs can achieve predictive performance that is competitive with, and in some cases marginally better than, the benchmarked classical approaches. Our results also reveal the effects of dataset size and circuit complexity on predictive performance and simulation time. We believe our findings will offer valuable insights for researchers in the energy domain who wish to incorporate quantum machine learning into their work.