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Energy and AI

A quantum neural network model for short term wind speed forecasting using weather data

Otto Menegasso Pires, Erick Giovani Sperandio Nascimento, Marcelo Albano Moret

Published Aug 9, 2025• 12 citations
Publisher

Abstract

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.

DOI
10.1016/j.egyai.2025.100588

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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.
Abstract Quantum Machine Learning (QML) is an emerging field at the intersection of quantum computing and machine learning that aims to enhance classical learning methods by leveraging principles such as superposition and entanglement. However, skepticism remains regarding its practical advantages, particularly due to the limitations of noisy intermediate-scale quantum (NISQ) devices. This study addresses these concerns through a systematic evaluation of Quantum Neural Networks (QNNs), quantum-inspired counterparts of Artificial Neural Networks (ANNs). We construct and analyze 12 distinct QNN configurations by combining two quantum feature maps with six entanglement strategies for ansatz design. Experiments conducted on a wind energy dataset show that QNNs can achieve competitive or superior performance compared to classical baseline models, with the best-performing configuration (QNN-3) reaching an R 2 score of up to 0.94 when forecasting wind power output using four input features. In addition, we provide a detailed analysis of simulation time across different configurations, highlighting trade-offs between model complexity and computational cost. These findings demonstrate the potential of QNNs as viable models for predictive tasks and contribute to the ongoing evaluation of QML in real-world applications.
Quantum Machine Learning (QML) is an emerging field at the intersection of quantum computing and machine learning, aiming to enhance classical machine learning methods by leveraging quantum mechanics principles such as entanglement and superposition. However, skepticism persists regarding the practical advantages of QML, mainly due to the current limitations of noisy intermediate-scale quantum (NISQ) devices. This study addresses these concerns by extensively assessing Quantum Neural Networks (QNNs)-quantum-inspired counterparts of Artificial Neural Networks (ANNs), demonstrating their effectiveness compared to classical methods. We systematically construct and evaluate twelve distinct QNN configurations, utilizing two unique quantum feature maps combined with six different entanglement strategies for ansatz design. Experiments conducted on a wind energy dataset reveal that QNNs employing the Z\mathbf{Z}Z feature map achieve up to 93%93 \%93% prediction accuracy when forecasting wind power output using only four input parameters. Our findings show that QNNs outperform classical methods in predictive tasks, underscoring the potential of QML in real-world applications.
Weather forecasting plays a critical role across various sectors, supporting strategic planning and reducing the impact of hazardous climatic conditions. However, the inherently chaotic and nonlinear nature of atmospheric systems limits the performance of conventional forecasting approaches, often resulting in prediction inaccuracies and heightened risk. This study proposes a quantum-driven predictive framework that exploits the computational strengths of Quantum Machine Learning (QML) and hybrid quantum–classical optimization to enhance the accuracy and efficiency of modern forecasting models. The framework employs Quantum Neural Networks (QNNs) combined with Variational Quantum Circuits (VQCs) to learn complex spatial–temporal dynamics embedded within large meteorological datasets. Through quantum feature encoding, key atmospheric indicators—including temperature, humidity, wind velocity, and barometric pressure—are projected into high-dimensional Hilbert spaces, enabling more expressive pattern extraction and robust predictive behavior. Simulation results reveal that the quantum-augmented approach surpasses traditional deep learning architectures in both training convergence and forecast precision when applied to extensive weather records. The hybrid design further supports scalability by intelligently distributing computational workloads between classical processors and quantum hardware. Overall, this research demonstrates the transformative potential of quantum computing in atmospheric modeling, offering a foundation for future real-time forecasting systems capable of managing the increasing complexity of global climate patterns.
Quantum Machine Learning (QML) presents as a revolutionary approach to weather forecasting by using quantum computing to improve predictive modeling capabilities. In this study, we apply QML models, including Quantum Gated Recurrent Units (QGRUs), Quantum Neural Networks (QNNs), Quantum Long Short-Term Memory(QLSTM), Variational Quantum Circuits(VQCs), and Quantum Support Vector Machines(QSVMs), to analyze meteorological time-series data from the ERA5 dataset. Our methodology includes preprocessing meteorological features, implementing QML architectures for both classification and regression tasks. The results demonstrate that QML models can achieve reasonable accuracy in both prediction and classification tasks, particularly in binary classification. However, challenges such as quantum hardware limitations and noise affect scalability and generalization. This research provides insights into the feasibility of QML for weather prediction, paving the way for further exploration of hybrid quantum-classical frameworks to enhance meteorological forecasting.
Merger of Quantum computing and Machine learning explores a new shift in artificial intelligence. Quantum neural networks and parameterized quantum circuits are tools that enable the merger of these two branches. Here, we use Quantum circuit enabled long short term memory (QLSTM) neural network to forecast time series data. We show the efficacy of quantum computing by conducting experiments on two different time series data sets. In our first experiment, we are predicting the amount of rainfall fall and the second experiment has electric load (power) prediction. Our dataset for rainfall prediction includes hourly information on the weather conditions i.e., wind speed, wind direction, minimum and maximum temperatures, and pressure with the amount of rain falls. For electric load (power generation) dataset, few of the features include amount of biomass, amount of fossil brown coal/ignite, amount of fossil coal derived gas, nuclear power, solar power, and the corresponding wind velocity forecasts. We compare the training as well as the test loss of classical Bidirectional LSTM (BILSTM) and the Quantum BILSTM and observe that LSTM based on quantum approach reduces both the training and test loss considerably when compared to its classical part with very few epochs of training.
Quantum Machine Learning (QML) presents as a revolutionary approach to weather forecasting by using quantum computing to improve predictive modeling capabilities. In this study, we apply QML models, including Quantum Gated Recurrent Units (QGRUs), Quantum Neural Networks (QNNs), Quantum Long Short-Term Memory(QLSTM), Variational Quantum Circuits(VQCs), and Quantum Support Vector Machines(QSVMs), to analyze meteorological time-series data from the ERA5 dataset. Our methodology includes preprocessing meteorological features, implementing QML architectures for both classification and regression tasks. The results demonstrate that QML models can achieve reasonable accuracy in both prediction and classification tasks, particularly in binary classification. However, challenges such as quantum hardware limitations and noise affect scalability and generalization. This research provides insights into the feasibility of QML for weather prediction, paving the way for further exploration of hybrid quantum-classical frameworks to enhance meteorological forecasting.