In this paper, a practical field of quantum technology known as “quantum technology, and weather prediction of smart grids”, and their future have been analyzed. Smart grids are among the significant fundamentals of a smart city and its supplying sources. So, factors such as weather prediction, or demand response are among important factors and have a crucial role. Also, experts should use highly accurate models to predict these factors such as weather prediction Therefore, when they are combined with Artificial Intelligence (AI), and Neural Networks (NN), they will be more efficient. However, in the smart grids of future Smart Sustainable Cities (SSCs) that the gathered big data has enormous amount, the classical AI, and ANN models will inefficient. Accordingly, using a higher technology known as “Quantum technology”, and its practical features, and potentials, has noticeable applications in smart grids, also, weather prediction. This paper presents weather prediction of smart grids, their regular methods, and quantum technology with its usage in the ladders. Respectively, it compares Quantum Neural Networks (QNN) method with other regular techniques that are ANN-based in the area of weather prediction. Finally, it is concluded that quantum technology and Quantum Neural Networks (QNN) have the potential to be combined with other techniques which leads to accurate models with ultrafast processing, and execution speed. Accordingly, they can be used in every field, and smart grids or weather predictions are not an exception.
In this paper, a practical field of quantum technology known as “quantum technology, and weather prediction of smart grids”, and their future have been analyzed. Smart grids are among the significant fundamentals of a smart city and its supplying sources. So, factors such as weather prediction, or demand response are among important factors and have a crucial role. Also, experts should use highly accurate models to predict these factors such as weather prediction Therefore, when they are combined with Artificial Intelligence (AI), and Neural Networks (NN), they will be more efficient. However, in the smart grids of future Smart Sustainable Cities (SSCs) that the gathered big data has enormous amount, the classical AI, and ANN models will inefficient. Accordingly, using a higher technology known as “Quantum technology”, and its practical features, and potentials, has noticeable applications in smart grids, also, weather prediction. This paper presents weather prediction of smart grids, their regular methods, and quantum technology with its usage in the ladders. Respectively, it compares Quantum Neural Networks (QNN) method with other regular techniques that are ANN-based in the area of weather prediction. Finally, it is concluded that quantum technology and Quantum Neural Networks (QNN) have the potential to be combined with other techniques which leads to accurate models with ultrafast processing, and execution speed. Accordingly, they can be used in every field, and smart grids or weather predictions are not an exception.
This paper presents a practical field of quantum technology and its contribution to smart grids, and their future in smart sustainable cities (SSCs). Smart grids are among the important basis of an SSC, so its factors such as forecasting, optimization, turbine design have a significant role. Based on quantum technology's amazing features, it has conspicuous applications in smart grids including weather prediction, or demand response forecasting. This paper represents SSCs, smart grids, and quantum technology and its use in the ladders. Then, defines the areas of smart grids in which quantum technology can make a noticeable development. Respectively, it compares quantum technology with other regular supercomputing technologies. Ultimately, quantum technology is a kind of technology that has a noticeable contribution potentiality to other technologies to make the world to be processed in a faster, and much more accurate way, and smart grids, SSCs are, also, in this future.
The use of computational intelligence has become commonplace for accurate wind speed and energy forecasting, however the energy-intensive processes involved in training and tuning stands as a critical issue for the sustainability of AI models. Quantum computing emerges as a key player in addressing this concern, offering a quantum advantage that could potentially accelerate computations or, more significantly, reduce energy consumption. It is a matter of debate if purely quantum machine learning models, as they currently stand, are capable of competing with the classical state of the art on relevant problems. We investigate the efficacy of quantum neural networks (QNNs) for wind speed nowcasting, comparing them to a baseline Multilayer Perceptron (MLP). Utilizing meteorological data from Bahia, Brazil, we develop a QNN tailored for up to six hours ahead wind speed prediction. Our analysis reveals that the QNN demonstrates competitive performance compared to MLP. We evaluate models using RMSE, Pearson’s R, and Factor of 2 metrics, emphasizing QNNs’ promising generalization capabilities and robustness across various wind prediction scenarios. This study is a seminal work on the potential of QNNs in advancing renewable energy forecasting, advocating for further exploration of quantum machine learning in sustainable energy research.
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.
Weather forecasting is a major challenge because of the unpredictable nature of atmospheric systems and the large number of interacting variables. Although traditional machine learning models have been used for weather prediction, they cannot capture the complex, nonlinear dynamics of weather. Recently, Quantum Machine Learning (QML) shows ability to improve computational speed and pattern recognition. Its practical application in weather prediction, however, is not well exploited. This work methodically analyzes QML algorithms for predicting weather states and compares their predictive performance with that of classical machine learning. We assembled extensive hourly meteorological data for Sylhet City, Bangladesh (24.9172∘N,91.8319∘E) over 185 days from Microsoft and IBM weather sites, totaling 4,504 data points. The dataset combines the main atmospheric parameters as temperature, real feel temperature, humidity, precipitation probability, visibility, dew point, and air pressure, from Microsoft and IBM weather sites. Four QML algorithms, including Quantum Random Forest (QRF), Quantum Decision Tree (QDT), Quantum Support Vector Machine (QSVM), and Quantum Neural Network (QNN) are trained and tested for five weather state classification, and compared to their classical counterparts named SVM, Random Forest, Decision Tree, and Neural Network. All quantum experiments were run on Qiskit simulators due to current hardware limitations. Results show that classical models performed slightly better than quantum models, and the difference was not very large.
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.
A promising combination of artificial intelligence and quantum computing, quantum neural networks (QNNs) have the potential to completely transform the way neural networks interpret and learn from data. QNNs can outperform classical neural networks in terms of speed, efficiency, and scalability by utilizing the special qualities of quantum physics, such as superposition, entanglement, and quantum parallelism. The basic ideas of QNNs, their structures, and the ways in which quantum algorithms can be incorporated into machine learning processes to improve problem-solving skills are all examined in this study. current developments in QNNs, emphasizing how they can be used for challenging applications including pattern recognition, optimization, and high-dimensional data space decision-making. We also go over the difficulties in creating scalable QNN models and the possible future applications of QNNs in artificial intelligence. By utilizing the potential of quantum computing, QNNs have the potential to advance artificial intelligence by providing answers to issues that are now intractable through traditional methods.