Electricity Price Forecasting Method Based on Quantum Immune Optimization BP Neural Network Algorithm

Zhang, Xuan; Hao, Qi; Qu, Wenjie; Ji, Xingquan; Xu, Baoxi

doi:10.1109/acpee51499.2021.9436865

Cited by 2 publications

(1 citation statement)

References 13 publications

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“…In [75], an electricity price forecasting model is presented using a quantum immune neural network (QINN) with a backpropagation algorithm, which was claimed to be tested by a power company in New Zealand. A quantum-inspired optimization technique that provides a robust non-linear mapping capacity opted to improve the performance of the classical BP algorithm, which is often trapped into local optimal solutions.…”

Section: Quantum Machine Learning-based Forecastingmentioning

confidence: 99%

Quantum computing for smart grid applications

Ullah

Eskandarpour

Zheng

et al. 2022

IET Generation Trans & Dist

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Computational complexities in modern power systems are reportedly increasing daily, and it is anticipated that traditional computers might be inadequate to provide the computation prerequisite in future complex power grids. In that given context, quantum computing (QC) can be considered a next‐generation alternative solution to deal with upcoming computational challenges in smart grids. The QC is a relatively new yet promising technology that leverages the unique phenomena of quantum mechanics in processing information and computations. This emerging paradigm shows a significant potential to overcome the barrier of computational limitations with better and faster solutions in optimization, simulations, and machine learning problems. In recent years, substantial progress in developing advanced quantum hardware, software, and algorithms have made QC more feasible to apply in various research areas, including smart grids. It is evident that considerable research has already been carried out, and such efforts are remarkably continuing. As QC is a highly evolving field of study, a brief review of the existing literature will be vital to realize the state‐of‐art on QC for smart grid applications. Therefore, this article summarizes the research outcomes of the most recent papers, highlights their suggestions for utilizing QC techniques for various smart grid applications, and further identifies the potential smart grid applications. Several real‐world QC case studies in various research fields besides power and energy systems are demonstrated. Moreover, a brief overview of available quantum hardware specifications, software tools, and algorithms is described with a comparative analysis.

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Section: Quantum Machine Learning-based Forecastingmentioning

confidence: 99%