code comments can help developers quickly understand code and reduce maintenance costs. However, due to the widespread phenomenon of code cloning and the complex syntax structure of code, existing comment generation m...
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As a kind of clean energy, photovoltaic power generation not only does not produce greenhouse gas emissions, but also helps to reduce the impact of global warming and climate change. However, due to the influence of w...
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As a kind of clean energy, photovoltaic power generation not only does not produce greenhouse gas emissions, but also helps to reduce the impact of global warming and climate change. However, due to the influence of weather conditions on photovoltaic power generation, its output fluctuates greatly, and its installation is scattered. these problems seriously limit the accuracy of photovoltaic power generation prediction. this paper proposes a reverse learning whale optimization algorithm for distributed photovoltaic virtual acquisition to improve the power prediction accuracy of distributed photovoltaic users. Firstly, a prediction model based on deep learning is built. Secondly, a reverse learning whale optimization algorithm is proposed for distributed photovoltaic virtual acquisition. Finally, the proposed method is simulated and verified.
Generative Adversarial Networks (GANs) perform well in continuous data generation, but face challenges in discrete sequence generation due to the non-differentiability of backpropagation. SeqGAN addresses this issue b...
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To address the challenges posed by random fluctuations in coal mine derivatives, renewable energy generation and load on the operation and scheduling of integrated energy systems, a two-stage stochastic optimization o...
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To address the challenges posed by random fluctuations in coal mine derivatives, renewable energy generation and load on the operation and scheduling of integrated energy systems, a two-stage stochastic optimization operation method for coalmine integrated energy systems is proposed. First, the characteristics of the derivatives and wind and solar resource utilization equipment are complete, and mathematical models and constraints are established. Second, a Monte Carlo simulation of random scene generation is used to establish the uncertainty model, and typical scenes are screened through scene reduction. Subsequently, in the first stage of optimization, constraints such as electricity prices and equipment power ramping were considered based on daily forecasting, and the unit output configuration was planned withthe goal of maximizing the consumption of wind and solar resources, while the second-stage optimization considered the random fluctuations of load, withthe goal of minimizing operating costs and coordinating flexible units to compensate for the first-stage optimization. Finally, using a coal mine in China as an example, the Yalmip toolbox and CPLEX solver were used to solve and verify that this method can effectively reduce costs.
Aiming at the problems that QT-based real-time process applications in Embedded Operating System operating system are slow to load Qt dynamic library and slow to parse Chinese fonts, which affect the startup speed of ...
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Withthe increasing scale of distributed photovoltaic integration into distribution networks, the uncertain fluctuation characteristics of PV output power under power reverse feeding conditions result in frequent fluc...
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Wind power is a renewable energy, and its power prediction plays an important role in operation. the traditional wind power prediction model has a lot of room for improvement. this paper proposes a wind power predicti...
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Wind power is a renewable energy, and its power prediction plays an important role in operation. the traditional wind power prediction model has a lot of room for improvement. this paper proposes a wind power prediction method based on improved grey Wolf algorithm to optimize multi-layer perceptron, in which multi-layer perceptron is a powerful nonlinear prediction method, aiming to improve the model effect from the perspective of selecting optimization algorithms. In this study, data preprocessing is carried out first, which includes outlier filtering, missing value filling and principal component analysis. After that, the multi-layer perceptron model is constructed and trained. Secondly, considering the limitation of searching ability of standard Gray Wolf algorithm, an improved Gray Wolf algorithm is proposed, the convergence factor is designed as the form of adaptive decline, and the position update formula is improved for parameter optimization of multi-layer perceptron model. Finally, the data set of a wind farm in western China is used to verify the experiment. the results show that the prediction effect of the multi-layer perceptron model optimized based on the improved Gray Wolf algorithm is better than other models on the verification set and the test set, and the average absolute error and the root-mean-square error are significantly reduced, which verifies the validity of the proposed method. the method proposed in this study improves the accuracy of wind power prediction and provides a new technical approach for the development and application of renewable energy.
Heat exchangers (HEs) are essential in process industries for efficient thermal energy transfer. their design and optimization are crucial for improving energy efficiency, reducing costs, and ensuring reliable system ...
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Automated program repair techniques address software errors, vulnerabilities, and defects through automation. Withthe rapid development of deep learning, deep learning-based automated repair techniques have improved ...
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In large-scale system production, the risk of component failures like hardware issues, software bugs, network disruptions, and memory errors is a concern. To mitigate this, human experts such as IT analysts, system en...
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ISBN:
(纸本)9798350371215
In large-scale system production, the risk of component failures like hardware issues, software bugs, network disruptions, and memory errors is a concern. To mitigate this, human experts such as IT analysts, system engineers, and infrastructure architects use system monitoring to detect and respond to failures. this study aims to integrate generative AI technology into self-healing systems to enhance the operations of large-scale systems and facilitate automatic repairs. the focus is on optimizing system functionality and efficiency at scale while reducing reactive tasks that require human intervention. Our proposed solutions involve leveraging generative AI for anomaly detection, codegeneration, debugging and auto generative report within self-healing systems. Furthermore, the automated response ansible scripts, generated by generative AI such as GPT-4 to create a comprehensive and efficient python code completion solution that enhances backend system functionality and repairs failing components.
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