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Wind Power Prediction Based on Machine Learning and Deep Learning Models

computers, Materials & Continua 2023年第1期74卷 715-732页

作者： Zahraa Tarek Mahmoud Y.Shams Ahmed M.Elshewey El-Sayed M.El-kenawy Abdelhameed Ibrahim Abdelaziz A.Abdelhamid Mohamed A.El-dosuky Faculty of Computers and Information Computer Science DepartmentMansoura UniversityMansoura35561Egypt Faculty of Artificial Intelligence Kafrelsheikh UniversityKafrelsheikh33511Egypt Faculty of Computers and Information Computer Science DepartmentSuez UniversitySuezEgypt Department of Communications and Electronics Delta Higher Institute of Engineering and TechnologyMansoura35111Egypt Faculty of Artificial Intelligence Delta University for Science and TechnologyMansoura35712Egypt Computer Engineering and Control Systems Department Faculty of EngineeringMansoura UniversityMansoura35516Egypt Department of Computer Science Faculty of Computer and Information SciencesAin Shams UniversityCairo11566Egypt Department of Computer Science College of Computing and Information TechnologyShaqra University11961Saudi Arabia Department of Computer Science Arab East CollegesRiyadh13544Saudi Arabia

Wind power is one of the sustainable ways to generate renewable *** recent years,some countries have set renewables to meet future energy needs,with the primary goal of reducing emissions and promoting sustainable growth,primarily the use of wind and solar *** achieve the prediction of wind power generation,several deep and machine learning models are constructed in this article as base *** regression models are Deep neural network(DNN),k-nearest neighbor(KNN)regressor,long short-term memory(LSTM),averaging model,random forest(RF)regressor,bagging regressor,and gradient boosting(GB)*** addition,data cleaning and data preprocessing were performed to the *** dataset used in this study includes 4 features and 50530 *** accurately predict the wind power values,we propose in this paper a new optimization technique based on stochastic fractal search and particle swarm optimization(SFSPSO)to optimize the parameters of LSTM *** evaluation criteria were utilized to estimate the efficiency of the regression models,namely,mean absolute error(MAE),Nash Sutcliffe Efficiency(NSE),mean square error(MSE),coefficient of determination(R2),root mean squared error(RMSE).The experimental results illustrated that the proposed optimization of LSTM using SFS-PSO model achieved the best results with R2 equals 99.99%in predicting the wind power values.

关键词： Prediction of wind power data preprocessing performance evaluation

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Dual Variational Generation Framework for Enhanced Cross-Modal Face Recognition

Dual Variational Generation Framework for Enhanced Cross-Mod...

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International Conference on Computing and Networking Technology (ICCNT)

作者： M Evany Anne M Brindha N Sivakumaran Computer Science and Engineering National Institute of Technology Tiruchirappalli India Instrumentation and Control Engineering National Institute of Technology Tiruchirappalli India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

One of the major challenges in face recognition is accurately identifying faces under varying lighting conditions, leading to a problem known as Heterogeneous Face Recognition (HFR). The objective is to integrate the capabilities of the modified Dual Variational Generator (DVG-Face) framework with traditional recognition methods, such as Local Binary Pattern Histogram (LBPH) and the HaarCascade framework, to provide a robust solution to the challenges of heterogeneous face recognition. This involves developing and implementing a model that successfully handles the complexity of HFR tasks utilizing synthetic images generated from Near-Infrared (NIR) and Visible (VIS) modalities. To guarantee identity consistency in the produced paired heterogeneous images, pairwise identity preserving loss is also used. The integration of Haar Cascades, the LBPH algorithm, and the DVG approach, along with person reidentification using the SSRL model, establishes a reliable framework for facial recognition in real-world security applications.

关键词： Histograms Face recognition Computational modeling Lighting Generators Complexity theory Security Reliability

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Acoustic feature extraction of wind turbine blades based on improved MFCC-GFCC 22

Acoustic feature extraction of wind turbine blades based on ...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Wang, Ziyi Chen, Yuanye Wang, Hua Wang, Haichao Fang, Fang School of Control and Computer Engineering North China Electric Power University Beijing China Huaneng Clean Energy Research Institute Beijing China Shaanxi Oceanx Technology Co. Ltd Xianyang China

ISBN: (纸本)9798331527471

As a critical component of wind turbines, the structural integrity of the blades is crucial for safe operation. To enhance the performance of health monitoring and fault detection systems, an acoustic feature extraction method is proposed to designed to aid in the development of a digital twin model for wind turbine blades (WTB). Our approach begins with a time index algorithm based on local maxima, which isolates single-blade data from audio recordings of the wind turbine. Then a blade acoustic signal feature extraction technique is developed by combining improved Mel Frequency Cepstral Coefficients (MFCC) and Gammatone Frequency Cepstral Coefficients (GFCC). This technique leverages anomaly contribution and the maximal information coefficient (MIC) as comprehensive indicators to filter out irrelevant and negative features. By multiplying and fusing the features, our method achieves feature complementarity and reduces computational complexity. The proposed method can be seamlessly integrated into digital twins for wind turbines. © 2024 IEEE.

关键词： Wind turbine blades

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Impact of Gale Weather Events on Wind Power Generation 1

Impact of Gale Weather Events on Wind Power Generation

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2024 1st International Conference on Smart Grids and Power Systems, ICSGPS 2024

作者： Zhang, Yuanhe Yang, Ming Wang, Bo Li, Menglin Yu, Yixiao School of Computer Science and Engineering Shandong University Shandong province Jinan China State Key Laboratory of Operation and Control of Renewable Energy & Storage Systems China Electric Power Research Institute Beijing China

Due to the influence of global warming, extreme wind weather occurs frequently, especially in extreme weather such as typhoons and cold waves, problems such as wind turbine shutdown, cutting out, and sudden changes in power generation power will occur, which brings great challenges to wind power forecast and safe and stable operation of the power grid. At present, the research and analysis of strong wind weather mostly stay at the single fan level, and the causes of abnormal changes in wind power output are mostly analyzed from the physical level. However, with the large-scale access of wind power to the power grid, the impact of extreme weather on power grid operation is becoming more and more significant, and it is particularly important to study the impact of strong wind weather events on wind power cluster output. This paper summarizes various types of wind power generation weather events, then screens key meteorological factors affecting power generation under different wind weather conditions based on Gaussian search and grey correlation weighting, and finally analyzes the meteorological impact on the actual operation of wind farm stations and clusters under typhoons and cold waves respectively, aiming at the problem of huge deviations in wind power forecast caused by wind. The next research direction and suggestions are put forward. © 2024 Published under licence by IOP Publishing Ltd.

关键词： Wind power

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Model Predictive control for Autonomous Driving: Comparing Kinematic and Dynamic Models of Tractor-Trailer Systems 27

Model Predictive Control for Autonomous Driving: Comparing K...

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27th IEEE International Conference on Intelligent Transportation Systems, ITSC 2024

作者： Vallinder, Gustav Martensson, Jonas Lima, Pedro F. Bhat, Sriharsha Scania Cv Ab SödertäljeSE-15132 Sweden School of Electrical Engineering and Computer Science Kth Royal Institute of Technology Division of Decision and Control Systems StockholmSE-10044 Sweden

ISBN: (纸本)9798331505929

Investigations of kinematic and dynamic models of tractor-trailer systems have historically been performed for stability analysis or state estimation. In this work, we present and evaluate kinematic and dynamic tractor-trailer models for model predictive control (MPC). We show in open-loop simulations that a kinematic and a dynamic model are equivalent at low speeds and short discretization time steps. A zero speed singularity and stiff dynamics prevents the usage of the dynamic model in control design, where discretization time steps are longer. A method of discretization is proposed to resolve the low speed feasibility of the dynamic model. In closed-loop simulations, the real-time applicability of the kinematic and dynamic models in a nonlinear MPC is verified. © 2024 IEEE.

关键词： Predictive control systems

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Time-Frequency Domain Fusion Enhancement for Audio Super-Resolution 24

Time-Frequency Domain Fusion Enhancement for Audio Super-Res...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Tian, Ye Wang, Zhe Sun, Jianguo Zhang, Liguo Hangzhou Institute of Technology Xidian University Hangzhou China China Industrial Control Systems Cyber Emergency Response Team Beijing China School of Computer Science and Technology Harbin Engineering University Harbin China

ISBN: (纸本)9798400706868

Audio super-resolution aims to improve the quality of acoustic signals and is able to reconstruct corresponding high-resolution acoustic signals from low-resolution acoustic signals. However, since acoustic signals can be divided into two forms: time-domain acoustic waves or frequency-domain spectrograms, most existing research focuses on data enhancement in a single field, which can only obtain partial or local features of the audio signal, resulting in limitations of data analysis. Therefore, this paper proposes a time-frequency domain fusion enhanced audio super-resolution method to mine the complementarity of the two representations of acoustic signals. Specifically, we propose an end-to-end audio super-resolution network. Including the variational autoencoder based sound wave super-resolution module, U-Net-based Spectrogram Super-Resolution Module, and attention-based Time-Frequency Domain Fusion Module. The first two modules can generate more high-frequency and low-frequency components for audio respectively. As a critical component of our method, time-frequency domain fusion module performs weighted fusion on the above two outputs to obtain a super-resolution audio signal. Compared with other methods, experimental results on the VCTK and Piano datasets in natural scenes show that the time-frequency domain fusion audio super-resolution model has a state-of-the-art bandwidth expansion effect. Furthermore, we perform super-resolution on the ShipsEar dataset containing underwater acoustic signals. The super-resolution results are used to test ship target recognition, and and the accuracy is improved by 12.66%. Therefore, the proposed super-resolution method has excellent signal enhancement effect and generalization ability. © 2024 ACM.

关键词： Spectrographs

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Machine Learning based Design of Energy Management System using Non-Intrusive Load Monitoring Strategy

Machine Learning based Design of Energy Management System us...

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2024 International Conference on control, Automation and Diagnosis, ICCAD 2024

作者： Shahid, Kamal Gulzar, Muhammad Majid Raza, Muhammad Ali Habib, Salman Ahmad, Hasnain University of The Punjab Institute of Electrical Electronics and Computer Engineering Lahore Pakistan King Fahd University of Petroleum & Minerals Department of Control & Instrumentation Engineering Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Dhahran31261 Saudi Arabia Department of Electrical Engineering Islamabad Pakistan

ISBN: (纸本)9798350361025

In this work, a Non-Intrusive Load Monitoring (NILM) system is designed for smart homes based on smart energy meters. The proposed solution simplifies the monitoring process by using a single set of sensors, in contrast to conventional systems that call for several sensors. From different appliances and their combinations, crucial electrical information, such as voltage, current, active power, reactive power, apparent power, power factor, and frequency was gathered. Proposed intelligent data analysis employing machine learning methods, particularly Artificial Neural Networks (ANN) and Deep Neural Networks (DNN), is the key innovation. These methods accurately categorize various appliance kinds, with DNN performing best in real-time circumstances. Additionally, there is a built-in email alert system that activates whenever there are odd electrical surges. The streamlined user interface, offers accurate identification of individual appliances and combinations in real-time forecasts based on past data. Future developments include a larger appliance data-set, improved categorization methods, load forecasting, and the creation of a smartphone app for real-time energy consumption data and automatic control, among other things. In a net-shell, this study offers an effective, single-sensorbased solution for load monitoring in smart homes, representing a substantial development in NILM technology. This technology claims to improve resource utilization and energy management in both domestic and commercial settings. © 2024 IEEE.

关键词： Deep neural networks

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GCN-LSTM Based Transient Angle Stability Assessment Method for Future Power Systems Considering Spatial-temporal Disturbance Response Characteristics

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Protection and control of Modern Power Systems 2024年第6期9卷 108-121页

作者： Shiwei Xia Chenhui Zhang Yahan Li Gengyin Li Linlin Ma Ning Zhou Ziqing Zhu Huan Ma the School of Electrical and Electronic Engineering North China Electric Power UniversityBeijing 102206China the State Grid Shandong Dispatch and Control Center Jinan 250003China the State Grid Shandong Electric Power Research Institute Jinan 250001China the Department of Computer Science Hong Kong Baptist UniversityHong Kong 999077China

Traditional transient angle stability analysis methods do not fully consider the spatial characteristics of the network topology and the temporal characteristics of the time-series ***,a data-driven method is proposed in this study,combining graph convolution network and long short-term memory network(GCN-LSTM)to analyze the transient power angle sta-bility by exploring the spatiotemporal disturbance char-acteristics of future power systems with high penetration of renewable energy sources(wind and solar energy)and power *** key time-series electrical state quantities are considered as the initial input feature quantities and normalized using the Z-score,whereas the network adjacency matrix is constructed according to the system network *** normalized feature quan-tities and network adjacency matrix were used as the inputs of the GCN to obtain the spatial features,reflecting changes in the network ***,the spa-tial features are inputted into the LSTM network to ob-tain the temporal features,reflecting dynamic changes in the transient power angle of the ***,the spatiotemporal features are fused through a fully con-nected network to analyze the transient power angle stability of future power systems,and the softmax activa-tion cross-entropy loss functions are used to predict the stability of the *** proposed transient power angle stability assessment method is tested on a 500 kV AC-DC practical power system,and the simulation results show that the proposed method could effectively mine the spatiotemporal disturbance characteristics of power sys-tems. Moreover, the proposed model has higher accuracy, higher recall rate, and shorter training and testing times than traditional transient power angle stability algo-rithms.

关键词： Future power system spatiotemporal disturbance characteristics transient power angle stabil-ity graph convolutional network long short-term memory network.

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Super Resolution of Unpaired MR Images Based on Domain Migration 18th

Super Resolution of Unpaired MR Images Based on Domain Migra...

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18th International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIH-MSP 2022

作者： Liu, Xiaodong Guo, Haipeng Liu, Huanyu Department of Automatic Test and Control School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China Information Countermeasure Technique Institute School of Computer Science and Technology Harbin Institute of Technology Harbin150080 China

ISBN: (纸本)9789819901043

High-resolution magnetic resonance imaging (MRI) is very important for doctors to judge the condition. At present, the depth learning method has been widely studied in the super-resolution reconstruction of MR images. However, due to the distribution difference between the real low-resolution MR images and the down sampled MR images used to train the super-resolution model under experimental conditions, the super-resolution performance of the super-resolution method is often seriously degraded when super-resolution is applied to the real low-resolution MR images. Blind super-resolution methods used to solve such problems in natural image super-resolution are lack of image content constraints. It is not advisable to introduce additional information that will interfere with doctors’ diagnosis in the super-resolution process of MR images. These two problems hinder the application of super-resolution method in real MR images. In order to solve these problems, an unpaired MR image super-resolution network based on domain migration is proposed. The proposed DMN can use a real set of low to high-resolution unpaired MR images as input, migrate the real low-resolution MR images to the down sampled MR image domain, and then super-resolution reconstruction is carried out through the paired super-resolution network obtained through supervision training. Experiments on fast MRI datasets verify the effectiveness of this method in solving the super-resolution problem of real MR images. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Magnetic resonance imaging

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Overview of bioinformatic tools to study viral infections

Overview of bioinformatic tools to study viral infections

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2023 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2023

作者： Zulian, Verdiana Garbuglia, Anna Rosa Fiscon, Giulia Paci, Paola National Institute for Infectious Diseases Lazzaro Spallanzani Irccs Virology Laboratory Rome Italy Sapienza University of Rome Department of Computer Control and Management Engineering Italy National Research Council Institute for Systems Analysis and Computer Science 'Antonio Ruberti' Rome Italy

ISBN: (纸本)9798350337488

microRNAs play an important role in post-transcriptional gene regulation. Recently, viral microRNAs have been discovered in several viruses, including Hepatitis B virus. This brief work explores bioinformatics tools for viral/host miRNA research and provides insights into the roles of miRNAs in HBV infection, offering an overview of this field, in order to facilitate the selection of the most suitable bioinformatics tools according to individual needs and research goals. © 2023 IEEE.

关键词： bioinformatics tools Hepatitis B virus microRNA target gene

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