The intermittent nature of wind speed poses challenges for its widespread utilization as an electrical power generation source. As the integration of wind energy into the power system increases, accurate wind speed forecasting becomes crucial. The reliable scheduling of wind power generation heavily relies on precise wind speed forecasts. This paper presents an extended work that focuses on a hybrid model for 24 h ahead wind speed forecasting. The proposed model combines residual Long Short-Term Memory (LSTM) and a quantum neural network that is studied by a quantum simulator, leveraging the support of NVIDIA Compute Unified Device Architecture (CUDA). To ensure the desired accuracy, a comparative analysis is conducted, examining the qubit count and quantum circuit depth of the proposed model. The execution time required for the model is significantly reduced when the GPU incorporates CUDA, accounting for only 8.29% of the time required by a classical CPU. In addition, different quantum embedding layers with various entangler layers in the quantum neural network are explored. The simulation results utilizing an offshore wind farm dataset demonstrate that the proper number of qubits and embedding layer can achieve favorable 24 h ahead wind speed forecasts.
The intermittent nature of wind speed poses challenges for its widespread utilization as an electrical power generation source. As the integration of wind energy into the power system increases, accurate wind speed forecasting becomes crucial. The reliable scheduling of wind power generation heavily relies on precise wind speed forecasts. This paper presents an extended work that focuses on a hybrid model for 24 h ahead wind speed forecasting. The proposed model combines residual Long Short-Term Memory (LSTM) and a quantum neural network that is studied by a quantum simulator, leveraging the support of NVIDIA Compute Unified Device Architecture (CUDA). To ensure the desired accuracy, a comparative analysis is conducted, examining the qubit count and quantum circuit depth of the proposed model. The execution time required for the model is significantly reduced when the GPU incorporates CUDA, accounting for only 8.29% of the time required by a classical CPU. In addition, different quantum embedding layers with various entangler layers in the quantum neural network are explored. The simulation results utilizing an offshore wind farm dataset demonstrate that the proper number of qubits and embedding layer can achieve favorable 24 h ahead wind speed forecasts.
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.
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.
This study investigates the potential of quantum machine learning to enhance the accuracy of typhoon forecasting, with a particular emphasis on its implications for photovoltaic (PV) and wind turbine systems in the context of extreme weather preparedness. A novel approach is proposed, integrating a quantum layer into a multi-model Long Short-Term Memory (LSTM) network architecture. Implemented using CUDA-Q, the quantum layer leverages quantum phenomena to process typhoon-related data more effectively. The proposed quantum-enhanced LSTM model is evaluated on a large dataset of typhoon observations. Results demonstrate that the model achieves promising accuracy in both typhoon track forecasting—yielding mean absolute errors (MAE) of 0.0188 and 0.0307 for latitude and longitude, respectively—and intensity forecasting, with MAEs of 0.0233 for wind speed, 0.0449 for wind direction, and 0.0022 for the 50-knot wind radius. This research contributes to the growing field of quantum machine learning and its practical applications, paving the way for future advancements in typhoon prediction and disaster resilience, particularly in optimizing the operation and robustness of PV and wind turbine systems in typhoon-prone regions.
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.
Modern renewable power operations can be enhanced by integrating deep neural networks, particularly for forecasting solar irradiance. Recent advancements in quantum computing have shown potential improvements in classical deep neural networks. However, current challenges with quantum hardware, such as susceptibility to noise and decoherence, pose risks to its practicality. Hybrid quantum neural networks (HQNNs) are found to mitigate these issues, especially when integrated with graphics processing unit (GPU)-based pipelines. This paper presents a comparative study of different software platforms for developing HQNNs, using multi-location very short-term solar irradiance forecasting as an example. A classical benchmark model is initially designed based on statistical analysis of a 10-minute resolution solar irradiance dataset, with its parameters further optimized using Bayesian Optimization. The experimental design of this paper includes a loss comparison between classical neural networks and HQNNs across different seasons and a performance comparison between Pennylane, Torchquantum, and CUDA Quantum (CUDA-Q) as HQNN development platforms. Experimental results show that HQNNs achieve up to a 92.30% improvement in testing loss compared to classical neural networks. Regarding HQNN development platforms, Pennylane shows an 81.54% testing loss reduction from classical models, Torchquantum shows a 90.34% improvement, and CUDA-Q shows a 92.30% improvement in testing loss. Implementing hardware acceleration libraries for GPU-based state vector simulation demonstrates an approximate 275% speedup in average latency per epoch, a 218% speedup in inference time, and a 10.20% improvement in testing loss compared to CPU-based simulations. CUDA-Q achieves a training time 2.7 times shorter and an inference time 2.9 times shorter compared to Pennylane, while it is 32.3 times faster in training and 31 times faster in inference compared to Torchquantum.
Time series forecasting involves analyzing historical data to predict future trends and is useful for reducing uncertainty in various phenomena. Recurrent Neural Network-based Long Short-Term Memory (LSTM) has been shown to be used for time series forecasting. However, the introduction of a hybrid quantum-classical model of LSTM, i.e., Quantum LSTM (QLSTM), opens up avenues for faster and more efficient computing and, therefore, is of significant potential. These potentials underscore the necessity for a comparative analysis of both models to assess their effectiveness and efficiency. This study evaluates the performance of QLSTM and classical LSTM models in time series forecasting. Through controlled parametric numerical experiments, the study examines the performance of both models using CPUs and GPUs in cloud environments. Our findings reveal significant improvements in the QLSTM model, with enhanced training accuracy and reduced test loss compared to the classical LSTM under similar epochs and batch sizes. The results demonstrate the superior efficiency of the QLSTM model in handling large, complex data sets compared to the classical LSTM model.