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4th IEEE International Conference on Digital Twins and Parallel Intelligence, DTPI 2024

作者： Hou, Guolian Guo, Zhiqiang Zhang, Tianhao North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798350349252

Accurate load forecasting is essential for optimizing the operation and energy management of integrated energy system (IES). The accuracy of load forecasting depends on both historical data and prediction methods. To address the limitations of traditional load forecasting methods such as inaccurate forecasting and single load, this study proposes a novel approach that combined digital twin (DT) with Transformer for accurate multiple loads forecasting of IES. Next, a virtual representation of the IES is constructed using DT to provide accurate data for load forecasting. The data is then processed and fed into the Transformer model for training, resulted in models with high forecasting performance. Lastly, simulations are used to assess the performance of the proposed model on a park level IES. The results indicate that the proposed model demonstrates high accuracy and can closely track the changes in actual load values. © 2024 IEEE.

关键词： Digital twin Integrated energy system Load forecasting Transformer

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Attention-based Vision Knowledge Adaptation for Constrained Continual Learning

Attention-based Vision Knowledge Adaptation for Constrained ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Guo, Bicheng Zhou, Conghao Liu, Haoyu He, Shibo Chen, Jiming Shen, Xuemin Zhejiang University College of Control Science and Engineering China University of Waterloo Department of Electrical and Computer Engineering Canada

ISBN: (纸本)9798350351255

The demand for continual machine learning in the context of limited computational resources and data availability is critical in the evolving landscape of the connected digital world. Current network applications predominantly rely on deep learning models that require labor/computation-intensive training processes. These models often struggle to effectively adapt to new data while preserving performance on previously acquired knowledge. In this paper, we introduce a lightweight framework for continual knowledge adaptation and learning designed to address these challenges. To prevent disruption of existing services, we propose an attention-based adapter that integrates seamlessly with the existing vision model to encode new incoming data. The weights of the original model are kept fixed during the adaptation process, ensuring the preservation of previously learned knowledge. Furthermore, to enhance learning efficiency and accelerate convergence with new data, we implement a knowledge fusion mechanism that facilitates interaction between existing knowledge and information from new data. Our framework is modular, enabling flexible deployment across distributed devices. The adapter and knowledge fusion module are implemented at each stage with minimal trainable parameters, optimizing resource usage. Extensive experiments and ablation studies validate the effectiveness of the proposed framework. © 2024 IEEE.

关键词： Adversarial machine learning

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Equivalent modeling and simulation of cascading dynamics via neural network algorithm for A Microgrid Network 8

Equivalent modeling and simulation of cascading dynamics via...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Chen, Xingyuan Zhao, Jingwei Hu, Yang Yang, Han Shi, Zhentao Chen, Zuo North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798331523558

Due to the complex structure of microgrids, there is currently a lack of an orderly modeling framework. This article proposes a dynamic equivalent modeling method for microgrids based on cascaded neural network models. Clustering the physical network of microgrids based on prior knowledge, and proposing a dynamic equivalent modeling method for each group using neural networks. The effectiveness of the method is verified through simulation using a 10-node microgrid clusters as the object. © 2024 IEEE.

关键词： Neural network models

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A Dynamic Adaptive Boiler Efficiency Prediction Model Based on VMD-ILSSVM-LSTM 8

A Dynamic Adaptive Boiler Efficiency Prediction Model Based ...

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8th IEEE International Conference on Energy Internet, ICEI 2024

作者： Lin, Jianjie Sun, Yujian Zhang, Yi North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798331523558

Accurate prediction of boiler efficiency is critical for the optimal operation of power plant combustion systems. This study presents a hybrid prediction model of boiler efficiency combining Variational Mode Decomposition (VMD), improved Least Squares Support Vector Machine (ILSSVM), and Long Short-Term Memory (LSTM) network. Initially, VMD is used to decompose and reconstruct the boiler efficiency sequence, effectively capturing both high- and low-frequency features. The LSTM network is then leveraged to model the low-frequency characteristics of the data, while the LSSVM is utilized for the accurate prediction of high-frequency components. In cases where the prediction error exceeds the predefined threshold, model update strategy is conducted by replacing, adding, or deleting LSSVM samples and updating corresponding model parameters. Finally, the low- and high-frequency components predicted by LSTM and improved LSSVM are linearly combined to form the final prediction result. The simulation results demonstrate that the proposed dynamic adaptive VMD-ILSSVM-LSTM model significantly improves prediction accuracy with the lowest MAE and RMSE values. © 2024 IEEE.

关键词： Variational mode decomposition

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Informativeness of Query Answers for Knowledge Bases (Extended Abstract) 37

Informativeness of Query Answers for Knowledge Bases (Extend...

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37th International Workshop on Description Logics, DL 2024

作者： Andolfi, Luca Cima, Gianluca Console, Marco Lenzerini, Maurizio Department of Computer Control and Management Engineering 25 Via Ariosto Rome00185 Italy

This extended abstract summarises our recent work on the informativeness of query answers over Description Logics Knowledge Bases (KBs). We introduce a framework to characterise the information that query answers for KBs can represent and under its lens we study the informative power of current query answering definitions across the literature. Moreover, we also present novel notions of certain and possible answers, showing that they are able to represent a meaningful form of information. Lastly, we study the data complexity properties for relevant problems associated to the answers we introduced. © 2024 Copyright for this paper by its authors.

关键词： Structured Query Language

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Parameter Sensitivity Analysis of Co-Decisions

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International Journal of Crowd Science 2022年第2期6卷 63-74页

作者： Li, Li Sun, Hongbo Yao, Xia School of Computer and Control Engineering Yantai University Yantai264005 China

The purpose of this study is to examine the influence of different parameters on the legitimacy rate and effective efficiency of crowd decision-making and to guide decision-making in real life. In this paper, a crowd decision representation method based on the preference domain is proposed for the large-scale simulation implementation of crowd decision in a crowd intelligence network, a simulation modeling is performed for the members participating in the decision, and a formal propulsion algorithm is perfected. Lastly, the influence of key parameters on the decision results is analyzed through a large-scale simulation experiment. This study analyzes the influence of key parameters, such as the number of candidates, number of voters, and voting legitimacy rate reference value, on the decision-making results and summarizes the selection range of key parameters under different results. Through the simulation experiment of crowd decision-making, this paper provides inspiration for researchers to explore the parameter sensitivity of crowd decision-making and provides guidance for crowd decision-making in social life. © The author(s) 2022.

关键词： Decision making

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Model Reference Adaptive control and Neural Network-Based Concentration control of CSTR 7

Model Reference Adaptive Control and Neural Network-Based Co...

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7th IEEE Advanced Information Technology, Electronic and Automation control Conference, IAEAC 2024

作者： Haroon, Husam E. Elfadel North China Electric Power University School of Control and Computer Engineering Beijing China

ISBN: (纸本)9798350339161

The paper discusses the concentration control in Continuous Stirred Tank Reactors (CSTR) using another CSTR as a reference model. The PI controller regulates the reference model, which adjusts the model reference adaptive controller (MRAC). The neural controller is then tuned with the model identified from the second CSTR. The controllers' performance is assessed through the utilization of MATLAB/SIMULINK aiming to illustrate and enhance the system's concentration and stability. The simulation results are compared with the Model Reference Adaptive controller MIT and Lyapunov rules. © 2024 IEEE.

关键词： Model reference adaptive control

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Hermite broad-learning recurrent neural control with adaptive learning rate for nonlinear systems

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Soft Computing 2024年第7-8期28卷 6307-6326页

作者： Hsu, Chun-Fei Chen, Bo-Rui Department of Electrical Engineering Tamkang University New Taipei City25137 Taiwan Department of Electrical and Computer Engineering Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan

Although conventional control systems are simple and widely used, they may not be effective for complex and uncertain systems. This study proposes a Hermite broad-learning recurrent neural network (HBRNN) with a wide network structure and an internal feedback loop that enables good approximation of system dynamics without the need for a large number of training parameters. Furthermore, a Hermite broad-learning recurrent neural control (HBRNC) with HBRNN as the main controller is proposed, which requires no prior knowledge about the system dynamics and has no off-line learning phase. All the HBRNN network parameters are updated according to parameter learning laws through the gradient descent approach. To prevent network parameter overtraining of the HBRNN, a discrete-type Lyapunov function is used to determine the least upper bound for the learning rates. Additionally, an adaptive learning rate (ALR) approach is designed to dynamically fine-tune the learning rates within these specified limits, thereby achieving an optimal convergence speed between network parameters and tracking error. Finally, the HBRNC system with ALR is applied to a chaotic circuit and a reaction wheel pendulum, and its effectiveness is validated through simulation and experimentation. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： Learning systems

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Out-of-Distribution Robustness Forecasting for Offshore Wind Power via Matching Based Transformer 22

Out-of-Distribution Robustness Forecasting for Offshore Wind...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Lu, Jianmou Chen, Yuanye Fang, Fang Xiao, Feng Liu, Zhijie School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331527471

Due to extreme external environments and changes in component health status, offshore wind turbines can generate out-of-distribution (OOD) data compared to normal operation. Traditional wind power forecasting (WPF) models are typically trained using historical data. Nevertheless, these models fail to maintain robustness with respect to OOD data. This paper introduces the matching based Transformer (MatTF) to mine shared knowledge from data across different distributions, thereby addressing the OOD problem. First, an algorithm for the detection of distribution boundaries is designed to partition the training data into subsets. Then, adaptive factors are added to the self-attention module in traditional transformer to solve the distribution matching problem. Finally, the effectiveness of the model is verified through case study comparing with other WPF models. © 2024 IEEE.

关键词： Wind forecasting

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control Interval Reconstruction of Floating Wind Turbine Based on Response Characteristics 22

Control Interval Reconstruction of Floating Wind Turbine Bas...

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22nd IEEE International Conference on Industrial Informatics, INDIN 2024

作者： Zhou, Jingfeng Song, Ziqiu Fang, Fang School of Control and Computer Engineering North China Electric Power University Beijing China

ISBN: (纸本)9798331527471

The oscillatory movement, aerodynamic damping effect, gyroscopic effect, and other phenomena in floating wind turbines result in distinct response characteristics of output and load under varying wind-wave coupling scenarios. Designing controllers within traditional control intervals using wind speed or wind-wave metrics is insufficient for fundamentally improving the wind turbine’s performance. Firstly, according to the response distribution of the floating wind turbine, the response characteristics in different scenarios are extracted based on the autoencoder. And the response characteristics are clustered to form a control interval with response as the measurement index, which provides a basis for the partitioning in the construction of digital twin models for floating wind turbines. Secondly, within these reconstructed control intervals, control parameters are optimized based on the specific efficiency improvement and load reduction requirements of each scenario. This process leads to the development of a variable parameter controller. Finally, simulation experiments using FAST in various wind-wave coupling scenarios demonstrate that the proposed variable parameter controller effectively stabilizes power output and reduces loads. © 2024 IEEE.

关键词： Wind turbines

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