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IEEE Access

QLID-Net: A Hybrid Quantum-Classical Neural Network for Robust and Data-Efficient Smart Grid Load Identification

Batuhan Hangun, Oğuz Altun, Önder Eyecioğlu

Published Jan 1, 2026• 1 citation
Publisher

Abstract

Non-Intrusive Load Monitoring (NILM) is an essential component of modern smart grid systems, enabling the disaggregation of total power consumption into appliance-level usage. Accurate appliance classification is critical for optimal power flow and uninterrupted grid operation. In state-of-the-art NILM applications, classical machine learning (ML) models require abundant labeled data. However, in real-world scenarios, limited data and measurement noise are common challenges. Quantum machine learning (QML), which leverages quantum mechanical properties, may offer advantages for NILM compared to classical ML. In this study, we propose QLID-Net (Quantum Load IDentification Network), a hybrid quantum-classical neural network for appliance classification in NILM. The proposed architecture consists of a classical encoder, a variational quantum circuit (VQC), and a classical decoder.We evaluated the model on a smart meter dataset (five appliance classes) across four experiments: data efficiency, classical/data noise robustness, baseline comparison, and quantum hardware noise robustness comparing it against XG-Boost, TabPFN, Random Forest, and a parameter-matched classical neural network. In the data efficiency experiment, the proposed model outperformed two classical models at 10 or fewer samples per class and significantly outperformed the parameter-matched classical neural network (p<0.001, Cohen’s d=1.80), with a crossover point at approximately 20 samples per class. Under additive noise (Gaussian, Uniform), the proposed model showed superior robustness, outperforming all classical baselines with up to 17.9% improvement over XGBoost and up to 21.3% improvement over TabPFN. In the baseline comparison, it achieved competitive performance with XGBoost and TabPFN (within 5.1%) and 100% training convergence compared to 70% for the classical neural network. Finally, in the quantum hardware noise tests, results showed that although QLID-Net can compete with the parameter matched classical neural network, it faces a performance decline. Our results identify practical regimes where quantum advantages are beneficial, especially for scenarios with limited data or measurement noise. This study contributes to the growing evidence of practical QML applications and paves the way for quantum-assisted hybrid models in NILM.

DOI
10.1109/access.2026.3668295

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Non-Intrusive Load Monitoring (NILM) is an essential component of modern smart grid systems, enabling the disaggregation of total power consumption into appliance-level usage. Accurate appliance classification is critical for optimal power flow and uninterrupted grid operation. In state-of-the-art NILM applications, classical machine learning (ML) models require abundant labeled data. However, in real-world scenarios, limited data and measurement noise are common challenges. Quantum machine learning (QML), which leverages quantum mechanical properties, may offer advantages for NILM compared to classical ML. In this study, we propose QLID-Net (Quantum Load IDentification Network), a hybrid quantum-classical neural network for appliance classification in NILM. The proposed architecture consists of a classical encoder, a variational quantum circuit (VQC), and a classical decoder.We evaluated the model on a smart meter dataset (five appliance classes) across four experiments: data efficiency, classical/data noise robustness, baseline comparison, and quantum hardware noise robustness comparing it against XG-Boost, TabPFN, Random Forest, and a parameter-matched classical neural network. In the data efficiency experiment, the proposed model outperformed two classical models at 10 or fewer samples per class and significantly outperformed the parameter-matched classical neural network (p<0.001, Cohen’s d=1.80), with a crossover point at approximately 20 samples per class. Under additive noise (Gaussian, Uniform), the proposed model showed superior robustness, outperforming all classical baselines with up to 17.9% improvement over XGBoost and up to 21.3% improvement over TabPFN. In the baseline comparison, it achieved competitive performance with XGBoost and TabPFN (within 5.1%) and 100% training convergence compared to 70% for the classical neural network. Finally, in the quantum hardware noise tests, results showed that although QLID-Net can compete with the parameter matched classical neural network, it faces a performance decline. Our results identify practical regimes where quantum advantages are beneficial, especially for scenarios with limited data or measurement noise. This study contributes to the growing evidence of practical QML applications and paves the way for quantum-assisted hybrid models in NILM.
Smart grid stability prediction is crucial for providing uninterrupted power and high maintainability while operating smart grids. State-of-the-art research focuses on using Machine Learning (ML) to solve smart grid stability prediction; however, the complex nature of smart grids that serve thousands of users poses challenges for classical ML. Quantum Machine Learning (QML) has emerged as a possible solution to overcome these challenges, making QML a promising approach for energy applications. While QML shows promise in energy research, its application to grid stability problems remains unexplored. In this study, we propose a regularization-enhanced hybrid quantum-classical neural network that combines a classical encoder-decoder architecture with a Variational Quantum Circuit (VQC). Our model achieves 92.25 % accuracy, competitive with parameter-matched classical neural networks while outperforming Gradient Boosting and Random Forest by3.0−4.25%3.0-4.25 \%3.0−4.25%. Noise robustness analysis shows that the proposed hybrid model demonstrates a slight advantage under dropout noise while maintaining comparable performance under Gaussian and uniform noise, outperforming tree-based methods across all noise types.
With increasing prevalence of distributed energy resources (DERs), the growing threat of cyber-physical attacks, and the need for real-time optimization present major challenges for modern smart grids. With challenges also come opportunities. Although classical machine learning (ML) models, such as deep neural networks (DNNs), have shown promise toward meeting some of these challenges, they often struggle with the computational complexity when vast volumes of grid data are involved. The problems are particularly acute in situations when real-time responses are essential. This paper proposes a framework that integrates classical and quantum neural networks (QNNs) to initiate a new paradigm of time-sensitive grid management. We explore the potential of quantum computing to enhance the capabilities of classical neural networks in two critical applications: (1) real-time anomaly detection for cybersecurity and (2) dynamic optimal power flow (OPF) management for grid stability. We propose a hybrid quantum-classical neural network framework suitable for near-term quantum devices, discuss the theoretical advantages in computational speed and model capacity, and address the significant challenges related to quantum hardware noise, algorithmic design, and data encoding. The framework is flexible enough to allow for futureproofing as QNNs and quantum computing hardware continue to evolve. Our analysis suggests that QNNs, even in their current nascent stage, hold great promise to revolutionize smart grid analytics by solving complex, time-sensitive classification and optimization problems that are currently difficult or intractable for classical methods. The study therefore lays the groundwork for a future smart grid that is more secure, efficient, and robust.
Accurate load forecasting is critical for effective energy management, infrastructure planning, and ensuring the stability of modern power systems. With growing interest in Quantum Computing (QC), recent efforts have demonstrated the applicability of Quantum Machine Learning (QML) across various domains, including supervised and unsupervised learning. However, the use of QML models for Time Series Forecasting (TSF) remains relatively underexplored. In this work, we present a complete framework for TSF based on Variational Quantum Algorithms (VQAs), capable of both single-step and multi-step ahead forecasting. We use tensor product encoding to encode the classical input samples into quantum states, and use the data re-uploading strategy to introduce temporal dependencies across each layer of the quantum circuit. Within this framework, we systematically evaluate multiple Variational Quantum Circuit (VQC) architectures to assess their suitability for forecasting problem. Among the tested configurations, thenLocaland its modified variantQuReUpconsistently outperformed other quantum ansatz designs. Notably, in recursive multi-step forecasting scenarios, these models not only matched but surpassed the performance of a classical Artificial Neural Network (ANN). For example, thenLocalmodel achieved a 8% reduction in mean absolute error (MAE) over ANN for a 24-hour horizon. These results underscore the potential of VQAs for efficient and accurate TSF, particularly in scenarios requiring short to medium-term predictions. The proposed framework offers a promising foundation for future research, including extensions to diverse datasets and deployment on real quantum hardware to study the impact of quantum noise and scalability.
Interconnection studies require solving numerous instances of the AC load or power flow (AC PF) problem to simulate diverse scenarios as power systems navigate the ongoing energy transition. To expedite such studies, this work leverages recent advances in quantum computing to find or predict AC PF solutions using a variational quantum circuit (VQC). VQCs are trainable models that run on modern-day noisy intermediate-scale quantum (NISQ) hardware to accomplish elaborate optimization and machine learning (ML) tasks. Our first contribution is to pose a single instance of the AC PF as a nonlinear least-squares fit over the VQC trainable parameters (weights) and solve it using a hybrid classical/quantum computing approach. The second contribution is to feed PF specifications as features into a data-embedded VQC and train the resultant quantum ML (QML) model to predict general PF solutions. The third contribution is to develop a novel protocol to efficiently measure AC-PF quantum observables by exploiting the graph structure of a power network. Preliminary numerical tests indicate that the proposed VQC models attain enhanced prediction performance over a deep neural network despite using much fewer weights. The proposed quantum AC-PF framework sets the foundations for addressing more elaborate grid tasks via quantum computing.
The modernization of power grids, transitioning into smart grids, demands innovative technologies to manage and optimize energy distribution effectively. As power systems continue to evolve into smart grids with distributed and dynamic architectures, efficient load balancing becomes critical. Classical methods often prove inadequate in handling the complexity and variability of smart grid data. Increasing popularity of renewable energy sources adds another layer of complexity. The integration of renewable energy sources and increasing demand volatility pose significant challenges for smart grid load balancing. Traditional machine learning (ML) methods, while promising, face limitations in real-time optimization and scalability. Quantum Machine Learning (QML), which leverages quantum computing principles like superposition and entanglement, offers a promising frontier for optimizing energy distribution and load balancing by enhancing established ML algorithms. This paper surveys and compares current research on the application of QML to smart grid load balancing, exploring quantum neural networks (QNNs), quantum support vector machines (QSVM), as well as quantum optimization algorithms like Quantum Approximate Optimization Algorithm (QAOA), to improve various aspects of load-balancing efficiency in smart grids. By encoding classical grid data into quantum states, QML models exploit superposition and entanglement to process high-dimensional datasets faster than classical methods.
Smart grids form the backbone of modern energy infrastructures. They require accurate power prediction to ensure efficient energy management and maintain grid stability. Recent advancements in quantum computing have provided motivation to explore quantum machine learning models for data driven prediction task. In this study, a quantum computing-based power prediction model is investigated using a Variational Quantum Circuit (VQC) to estimate the AC power output in a solar-based smart grid system. The proposed model is studied using a simulation software created using Qiskit framework and real-world solar dataset is used as input. To enable a holistic comparison, the classical regression models, including Linear Regression, Ridge Regression, Random Forest, and XGBoost, are used as baseline models. Experimental findings indicate that classical models have lower prediction errors and much faster execution times. Conversely, the quantum model can only capture the broad patterns of the data because of the constraints that relate to Noisy Intermediate-Scale Quantum (NISQ) devices such as circuit noise, limited qubit count, and optimization limitations. Furthermore, a multi-criteria evaluation is conducted to compare the classical and quantum approaches based on prediction accuracy, execution time, model complexity, and scalability. The findings provide valuable insights into the feasibility of applying quantum computing techniques to smart grid prediction tasks and highlight both the potential and the limitations of quantum machine learning for real-world energy systems.