IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData)
Quantum Machine Learning for Electricity Theft Detection: an Initial Investigation
Electricity theft could result in massive economic loss and cause potential security problems to the grids. There-fore, it is of great importance to detect electricity theft from both an economic and social perspective. With the help of Advanced Metering Infrastructure (AMI) in smart grids, now it is possible to detect theft activities through analyzing the recorded electricity consuming data using advanced techniques, such as machine learning. However, training a learning model, especially for the ones with a huge amount of parameters, is always resource-consuming, and users could encounter performance bottlenecks due to hardware limitations. To improve the problem, using quantum computing for machine learning tasks has shown to be an effective approach to speed up the learning process. However, the relevant techniques have not been studied in the domain of energy yet. As a pioneering exploration, in this work, we are trying to leverage a quantum classification algorithm to classify the electricity dataset and on that basis to perform a case study on theft activity detection. Specifically, we have given the details of our design in the study, and our experimental results show that the prediction accuracy can be significantly improved with increasing training epochs, demonstrating the feasibility of quantum machine learning for energy data analysis.
Electricity theft could result in massive economic loss and cause potential security problems to the grids. There-fore, it is of great importance to detect electricity theft from both an economic and social perspective. With the help of Advanced Metering Infrastructure (AMI) in smart grids, now it is possible to detect theft activities through analyzing the recorded electricity consuming data using advanced techniques, such as machine learning. However, training a learning model, especially for the ones with a huge amount of parameters, is always resource-consuming, and users could encounter performance bottlenecks due to hardware limitations. To improve the problem, using quantum computing for machine learning tasks has shown to be an effective approach to speed up the learning process. However, the relevant techniques have not been studied in the domain of energy yet. As a pioneering exploration, in this work, we are trying to leverage a quantum classification algorithm to classify the electricity dataset and on that basis to perform a case study on theft activity detection. Specifically, we have given the details of our design in the study, and our experimental results show that the prediction accuracy can be significantly improved with increasing training epochs, demonstrating the feasibility of quantum machine learning for energy data analysis.
Thefts of electricity are a plague on smarter grids and dispersed generation of energy from PV systems, because smart meters could be tampered with. The identification of theft at the DG side by utilising typical ML algorithms is hard to achieve and is not appropriate for high-dimensional imbalanced data. To solve these problems, a hybrid Quantum DL model is designed using the QVC and DRC method and applied by a QML technique. The QVC consists of an angle-embedding layer to encode the classical neural outputs into the qubits and a set of trainable parameters of rotational and CNOT gates for the noise reduction on the NISQ devices. If there is still noise, the DRC layer will re-upload the data iteratively to compensate for it. The data set 'Smart Grid Theft Detection' is retrieved from Kaggle and the features in it are divided into 80% training and 20% testing sets. The FH-QVC-DRC model has an accuracy of 88% which is higher than standard algorithms like the XGBOOST and LIGHTGBM with 86% and 73% accuracy, respectively. Furthermore, the Entanglement quantum layer is proposed to boost the performance by using the quantum parallelism, quantum interference and secure state correlations. The developed model is called FH-QVC-DRC-ENT with an accuracy of 91 %. The visualisation, the performance indicators and the prediction output demonstrate that this quantum model, which uses entanglement, is able to better classify events of theft and non-theft, than the other models.
The stability of a smart grid architecture is one of the most important parameters to assess its effectiveness and reliability. As a result of increased industrialization and use of renewable energy, predicting stability with respect to different scenarios becomes increasingly important to mitigate potential instabilities. Therefore, employing machine learning approaches to forecast system stability is crucial for sustainability. Traditionally, classical computers have been employed for machine learning-based methods; however, the proliferation of producers, consumers, and prosumers in newly built smart grid structures generates massive volumes of data, posing significant challenges for traditional computational methods in processing and analyzing these data efficiently. Quantum computing offers a promising solution for handling large amounts of data generated by smart grids, potentially leading to more accurate and efficient stability predictions. This study investigates a benchmark-based approach by comparing classical machine learning with a quantum machine learning technique to predict the stability of the smart grid. We utilized the "Electrical Grid Stability Simulated Data" dataset, focusing on a 4-node architecture smart grid. We approached this task as a classification problem, comparing the performance of a classical Support Vector Machine (SVM) model with a quantum machine learning approach, namely the Variational Quantum Classifier (VQC). Both methods were tested and evaluated on the basis of their ability to classify the grid’s stability into "stable" and "unstable" categories. Our experimental results demonstrate the potential advantages and limitations of quantum computing in improving smart grid stability forecasting, and energy research in general.
Quantum computers make it possible to solve hard planning problems, accurately simulate how grids change over time, and improve the efficiency of grid activities. Quantum algorithms have changed the way grids are managed, how demand is predicted, and how energy is distributed in real time. In addition, AI technologies like machine learning, deep learning, and others improve quantum computing by looking at the huge amounts of data that smart power grids create. Using quantum mechanics in AI models and algorithms makes it possible for advanced control systems to handle grid processes automatically, adapt to changing conditions, and quickly fix problems. This research looks into how these technologies could be used, what problems they might cause, and what areas of research should be done in the future to help build safe, resilient, and efficient energy infrastructure. When quantum computing and artificial intelligence come together, they will change the energy business right away. This will create a power grid environment that is smarter, cleaner, and more resilient.
Fraud detection is a Crucial aspect of securing financial systems and protecting against fraudulent activities. This paper presents a new method for Detecting fraud, combining quantum-inspired feature engineering and neural network modeling, which achieves a classification accuracy of 92%, with a precision and recall of 0.92 for non-fraudulent transactions. Utilizing QuantumCircuit from Qiskit, the approach encodes input features into quantum states through angle encoding, improving data representation and capturing complex feature interactions such as entanglement between qubits. The method employs a Quantum Neural Network (QNN) for classifying records and effectively identifying fraudulent transactions. Through real-world dataset experiments, we demonstrate the efficacy of the proposed approach, achieving a precision of 0.65 and recall of 0.68 for fraudulent transactions, while maintaining a false positive rate below 10%. This research leverages advanced machine learning techniques, including balanced sampling strategies to address class imbalance and hyperparameter tuning with Grid-SearchCV for optimal model performance. The paper presents a comprehensive evaluation of the approach, highlighting its strengths in adapting to dynamic and complex environments. Quantum-inspired methods offer the potential to significantly enhance fraud detection systems. However, challenges such as computational complexity and resource requirements remain areas for further investigation. Future research will aim to scale these models for larger datasets and investigate their applicability in fields such as healthcare and e-commerce. In conclusion, our work demonstrates the promising use of quantum-enhanced machine learning for fraud detection. This innovative approach paves the way for developing advanced detection systems that can effectively respond to the evolving landscape of fraudulent activities. Future studies could concentrate on refining these techniques and exploring their potential applications in other areas to further harness the advantages of quantum computing in machine learning.
Quantum computers can solve difficult optimization issues, unlike regular computers. The proposed system optimizes smart grid energy distribution, load balancing, and resource allocation using quantum annealing and Grover's method. Quantum optimization should boost processing speed and accuracy. Quantum algorithms optimize electricity flow, mitigate transmission loss, and boost grid efficiency. By monitoring real-time data and changing loads, dynamic load balancing reduces smart grid bottlenecks and optimizes resource utilization. Machine learning algorithms will precisely forecast energy demand, enhancing grid control and resource distribution. Quantum computing and machine learning enhance smart grid management. From this connectivity, the smart grid gains exceptional efficiency, dependability, and agility, providing a more robust and environmentally friendly energy infrastructure.
The decentralization and softwarization of modern industrial control systems such as the electric grid has resulted in greater efficiency, stability and reliability but these advantages come at a price of higher likelihood of cyberattacks due to the resulting increase in cyberattack surface. Traditional cyberattack detection techniques such as rule-based anomaly detection have an important role to play in first response. However, given the data-rich environment of the modern electric grid, current research thrusts are focused on integrating data-driven machine learning techniques that automatically learn to detect anomalous modes of operation and predict the presence of new attacks. Quantum machine learning (QML) is a subset of machine learning that aims to leverage quantum computers to obtain a learning advantage by means of a training speed-up, data-efficiency, or other form of performance benefit. Questions remain regarding the practical advantages of QML, with the vast majority of existing literature pointing to its greater utility when applied to quantum data rather than classical data, which within a smart grid environment include TCP/IP packets or telemetry measurements. In this paper, we explore a scenario where quantum data may arise in the smart grid, and exploit a quantum algorithmic primitive previously proposed in the literature to demonstrate that in the best-case, QML can provide accuracy advantages of >25 percentage points when predicting anomalies.