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Electric Vehicle Charging Navigation Strategy Based on Data driven and Deep Reinforcement Learning 21

Electric Vehicle Charging Navigation Strategy Based on Data ...

引用

5th International conference on Control Engineering and Artificial Intelligence, CCEAI 2021

作者： Chen, Liangliang Yang, Fengkun Wu, Shengjun Xing, Qiang Nari Group Corporation State Grid Electric Power Research Institute Nanjing China State Grid Jiangsu Electric Power CO. Ltd Nanjing China School of Electrical Engineering Southeast University Nanjing China

ISBN: (纸本)9781450388870

With the popularization and popularization of electric vehicles, the interaction between electric vehicles and power grid and transportation networks is getting deeper and deeper. Considering the multi-data fusion characteristics of dynamic driving behavior and random charging behavior of electric vehicles and the complexity of multi-system modeling, this paper proposes a charging navigation strategy for electric vehicles based on Data-driven mode and deep reinforcement learning. Firstly, the actual operation data of electric vehicles collected by "electric vehicle cluster optimization energy storage cloud platform"are modeled and mined to obtain the driving, charging data and urban charging station data of electric vehicles. Secondly, the charging navigation model is established by introducing deep reinforcement learning method. The real-time data of "electric vehicle-charging station-traffic network"is used as the state space of deep Q-network algorithm, and the allocation of charging stations is regarded as the executive action of agent. The optimal action-value function is calculated by establishing the reward mechanism of driving on the way and after arriving at the station, so as to recommend the optimal charging station of return value and plan the driving path for vehicle owners. Finally, a multi-scene simulation example is designed to verify the feasibility and effectiveness of the proposed method © 2021 ACM.

关键词： Navigation

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Time Series Prediction of day-ahead Photovoltaic Power Based on Data-driven 4

Time Series Prediction of day-ahead Photovoltaic Power Based...

引用

4th IEEE China International Youth conference on Electrical Engineering, CIYCEE 2023

作者： Zhang, Mingyue Han, Yang Yan, Chaofeng Yang, Ping Wang, Congling University of Electronic Science and Technology of China School of Mechanical and Electrical Engineering Chengdu611731 China

ISBN: (纸本)9798350305562

This paper introduces a comprehensive forecasting model that integrates kernel principal component analysis (KPCA), grey wolf optimizer (GWO) and light gradient boosting machine (LightGBM), which aims to address the challenges posed to grid scheduling by large-scale photovoltaic (PV) grid connection. Firstly, KPCA is used to identify key factors influencing PV power, selecting correlated meteorological data as relevant features. These features and historical power data are used to train the LightGBM model. GWO optimization is applied to prevent the model from getting stuck in local optima, resulting in highly accurate ultra-short-term PV power predictions. The simulation results demonstrate that the GWO-optimized LightGBM significantly enhances prediction accuracy, reducing the mean absolute percentage error (MAPE) by 4.482% and the root mean square error (RMSE) by 0.289 kW. Furthermore, this proposed model surpasses XGBoost, achieving a reduction in MAPE and RMSE by 7.324% and 0.394 kW, respectively. These findings suggest that the proposed model effectively captures the variability in photovoltaic power and delivers more consistent prediction results. This research represents a promising avenue for further exploration in the field of ultra-short-term PV power prediction. © 2023 IEEE.

关键词： Forecasting

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Dynamic modeling and optimization of Robotic Fish Based on Passive Flexible Mechanism 18

Dynamic Modeling and Optimization of Robotic Fish Based on P...

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IEEE International conference on Mechatronics and Automation (IEEE ICMA)

作者： Zou, Qianqian Lu, Ben Fu, Yuzhuo Liao, Xiaocun Zhang, Zhuoliang Zhou, Chao Chinese Acad Sci Inst Automat State Key Lab Management & Control Complex Syst Beijing 100190 Peoples R China Univ Chinese Acad Sci Sch Artificial Intelligence Beijing 100049 Peoples R China

ISBN: (纸本)9781665441018

This paper proposes a modeling method for a robotic fish with elastic tail, which is driven by a motor in head and uses a spring steel sheet as a passive flexible joint, followed by a rigid caudal fin. The pseudo-rigid-body model (PRBM) is firstly used to analyze the flexible joint, then the fluid force acting on the robotic fish is divided into the pressure on links and cross-sectional resistance. Further, a dynamic model is established by the Lagrangian dynamic method to solve the movement of the robotic fish. And the model is utilized to investigate the influence of joint stiffness on propulsion performance. For different frequencies, the optimal joint stiffness is calculated to maximize the swimming speed. These optimized speeds are all higher than using rigid joints, and a maximum speed increase of 033 body length per second is reached at the largest simulation frequency 10Hz. In addition, the swimming speed can be maximized at the same input power by adjusting the stiffness of the joint. These findings prove the effectiveness of passive flexible joints in optimizing the performance of the robot fish.

关键词： robotic fish dynamic modeling flexible joint

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Unit Output optimization of Integrated Energy System Based on Digital Twin and Reinforcement Learning 5

Unit Output Optimization of Integrated Energy System Based o...

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5th IEEE conference on Energy Internet and Energy System Integration, EI2 2021

作者： Jiao, Fei Wang, Xinying Zhao, Rixiao Liu, Fengkui Sun, Qingkai Wang, Xiaojun Artificial Intelligence Application Research Department China Electric Power Research Institute Beijing China School of Electrical Engineering Beijing Jiaotong University Beijing China

ISBN: (纸本)9781665434256

The integrated energy system is different from the power system in that it integrates a variety of energy forms and coupling devices. The structure and scale are more complex, and the collection of system parameters is brutal. In this case, how to optimize the unit output to reduce the operation cost and maintain the safe and stable operation of the system has become an important research direction. This paper presents a method to build a virtual environment of an integrated energy system based on digital twin technology and find the optimal output strategy of the unit through a deep reinforcement learning decision model. Firstly, the real-time operation data of the integrated energy system are collected by multi-dimensional sensors to build a digital twin virtual environment. Secondly, the real-time output of the unit is simulated through the digital twin, which provides the relevant operation parameter basis for the follow-up deep reinforcement learning decision-making. Finally, the deep reinforcement learning decision model is used to find the optimal strategy for unit output, and the information is fed back to the terminal controller in real-time through the digital twin to guide the output of the integrated energy system unit. The simulation results show that the proposed method can effectively improve the unit output optimization accuracy of the integrated energy system and reduce the system operation cost. © 2021 IEEE

关键词： Reinforcement learning

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8th International conference on Applied Cognitive Computing, ACC 2024, and 26th International conference on Artificial Intelligence, ICAI 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

8th International Conference on Applied Cognitive Computing,...

引用

8th International conference on Applied Cognitive Computing, ACC 2024, and 26th International conference on Artificial Intelligence, ICAI 2024, held as part of the World Congress in Computer Science, Computer Engineering and Applied Computing, CSCE 2024

ISBN: (纸本)9783031856273

The proceedings contain 37 papers. The special focus in this conference is on Applied Cognitive Computing. The topics include: Assessing Information Influence for Node Attribute Prediction;in-Vehicle Sensing Platform for the Inference of Older Drivers’ Mild Cognitive Condition;infusing Human Feedback into Intermediate Prompting Steps of Large Language Models;experimental Analysis of Contemporary Trends, Performance, and Limitations in Graph Embeddings: A Concise Review;a Secure Self-Adaptive System in Applications of Cognitive Computing;revisiting the Nexus Between Handwriting and Personality: Graphology;from Static Graph Attention Generation to Dynamic Graph Attention Coefficient;An Integrated Approach for AI-Assisted Survey Systems Using Deterministic and Nondeterministic Models;a Voxel-Representation-Based Data Generator of Adversarial Objects for Robotic Manipulators;an Analysis of Different Information Granularity Distribution Protocols to Improve Consistency in Intuitionistic Reciprocal Preference Relations;enhancing Differential Evolution for Neural Network optimization Through Boundary Individual Consideration;predictive modeling of Shading Effects on Photovoltaic Panels Using Regression Analysis;enhancing Cultural Algorithm Guided Policy Gradients with Parameter-Based Exploration Through Topographic Knowledge and Adaptive Weighting;Enhancing Clinical Documentation Through NLP-driven Disease Categorization and Visualization: A Case Study Utilizing the Llama2 AI Model;automatic Modulation Recognition: A Hybrid Approach Using Deep Learning and K-Means Clustering;An ISR Asset Planning Application;causal Discovery with Interactive Human Inputs;implementation of a Lightweight Gradient Descent Trained Expert System Algorithm;Leveraging JSBSim and Gymnasium: A Reinforcement Learning Approach for Air Combat simulation;orthogonal Activation Functions in Neural Networks: Utilizing Chebyshev, Legendre, and Hermite Polynomials;XAI-Based Assessment of Software Vulnerabil

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AUTOREGRESSIVE DYNAMICS MODELS FOR OFFLINE POLICY EVALUATION AND optimization 9

AUTOREGRESSIVE DYNAMICS MODELS FOR OFFLINE POLICY EVALUATION...

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9th International conference on Learning Representations, ICLR 2021

作者： Zhang, Michael R. Le Paine, Tom Nachum, Ofir Paduraru, Cosmin Tucker, George Wang, Ziyu Norouzi, Mohammad University of Toronto Canada DeepMind Google Brain

Standard dynamics models for continuous control make use of feedforward computation to predict the conditional distribution of next state and reward given current state and action using a multivariate Gaussian with a diagonal covariance structure. This modeling choice assumes that different dimensions of the next state and reward are conditionally independent given the current state and action and may be driven by the fact that fully observable physics-based simulation environments entail deterministic transition dynamics. In this paper, we challenge this conditional independence assumption and propose a family of expressive autoregressive dynamics models that generate different dimensions of the next state and reward sequentially conditioned on previous dimensions. We demonstrate that autoregressive dynamics models indeed outperform standard feedforward models in log-likelihood on heldout transitions. Furthermore, we compare different model-based and model-free off-policy evaluation (OPE) methods on RL Unplugged, a suite of offline MuJoCo datasets, and find that autoregressive dynamics models consistently outperform all baselines, achieving a new state-of-the-art. Finally, we show that autoregressive dynamics models are useful for offline policy optimization by serving as a way to enrich the replay buffer through data augmentation and improving performance using model-based planning. © 2021 ICLR 2021 - 9th International conference on Learning Representations. All rights reserved.

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Process optimization of a pre-medication process in inpatient hospital care with 2D- and 3D-modeling software - an as-is process to-be process comparison

Process optimization of a pre-medication process in inpatien...

引用

ACS/IEEE International conference on Computer Systems and Applications

作者： Lisanne Kremer Robert Gutu Lea Leeser Bernhard Breil Thomas Lux Faculty of Health Care Niederrhein University of Applied Sciences Krefeld Germany Faculty of Health Care Niederrhein University of Applied Science

ISBN: (纸本)9798350310092

The hospital is a place of high complexity, high risk and intensive use of resources. In addition, processes are highly individual, non-standardized and vary from hospital to hospital. Thus, process modification is challenging and difficult to implement. Pre-medication is one of these complex and individual hospital processes. 2D tools such as the event-driven process chain, which can be represented with ARIS, and Ema WorkDesigner support digital production planning and prospective ergonomics as well as productivity assessment by providing an efficient and accurate approach to 3D human simulation of manual and semi-automatic activities. For this purpose, the pre-medication process in a hospital presented vulnerabilities identified. The weak points: an incomplete patient record as well as a crowded waiting area can potentially be minimized by process optimization. The simple representation in the 2D tool and the more complex modification using emaWD allow an analysis of these processes and identification of solution approaches, such as the creation of a waiting room and a modified process flow for the patient file. 2D and 3D tools should not be understood as competing, but as complementary.

关键词： Productivity Analytical models Solid modeling Three-dimensional displays Hospitals Computational modeling Production planning

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Data driven Optimal Coil Batching for Annealing Lines with Eligibility Constraints

Data Driven Optimal Coil Batching for Annealing Lines with E...

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IEEE International conference on Big Data

作者： Dong-Hyun Kang Jeong-Hwa Lee Tae-Sun Yu Yushin Lee Pukyong National University Busan Republic of Korea

ISBN: (纸本)9781665480468

This study proposes a modeling and scheduling methodology to solve the optimization problem of base (an annealing facility) reflecting various field requirements. Our main intention is to achieve a virtual loading simulation and instruction automation. In order to increase the productivity in an annealing line, the process should be executed by loading as many coils as possible into the base. Nonetheless, when loading into the base, several characteristics of both coils and base, such as subdivided an annealing cycle as well as process time, and so on, must be taken into consideration. As a result, we expect to build big data by collecting on-site facility data of the annealing line based on real-time IoT technology and industry standard protocols.

关键词： Coils Industries Productivity Annealing Job shop scheduling Loading Big Data

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Data-driven Model Invalidation for Unknown Lipschitz Continuous Systems via Abstraction

Data-Driven Model Invalidation for Unknown Lipschitz Continu...

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American Control conference (ACC)

作者： Jin, Zeyuan Khajenejad, Mohammad Yong, Sze Zheng Arizona State Univ Sch Engn Matter Transport & Energy Tempe AZ 85281 USA

ISBN: (数字)9781538682661

ISBN: (纸本)9781538682661

In this paper, we consider the data-driven model invalidation problem for Lipschitz continuous systems, where instead of given mathematical models, only prior noisy sampled data of the systems are available. We show that this data-driven model invalidation problem can be solved using a tractable feasibility check. Our proposed approach consists of two main components: (i) a data-driven abstraction part that uses the noisy sampled data to over-approximate the unknown Lipschitz continuous dynamics with upper and lower functions, and (ii) an optimization-based model invalidation component that determines the incompatibility of the data-driven abstraction with a newly observed length-T output trajectory. Finally, we discuss several methods to reduce the computational complexity of the algorithm and demonstrate their effectiveness with a simulation example of swarm intent identification.

关键词： Data models Mathematical model Trajectory Noise measurement Heuristic algorithms Computational modeling Computational complexity

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On Affine Policies for Wasserstein Distributionally Robust Unit Commitment

On Affine Policies for Wasserstein Distributionally Robust U...

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IEEE conference on Decision and Control

作者： Youngchae Cho Insoon Yang Department of Electrical and Computer Engineering ASRI SEPRI Seoul National University Seoul South Korea

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

his paper proposes a unit commitment (UC) model based on data-driven Wasserstein distributionally robust optimization (WDRO) for power systems under uncertainty of renewable generation as well as its tractable exact reformulation. The proposed model is formulated as a WDRO problem using an affine policy, which nests an infinite-dimensional optimization problem and satisfies the non-anticipativity constraint. To reduce conservativeness, we develop a novel technique that defines a subset of the uncertainty set with a probabilistic guarantee. Subsequently, the proposed model is recast as a semi-infinite programming problem that can be efficiently solved using existing algorithms. Notably, the scale of this reformulation is invariant with sample size. As a result, samples can be easily incorporated without using a sophisticated decomposition algorithm regardless of their number. Numerical simulations on 6- and 24-bus test systems demonstrate the economic and computational efficiency of the proposed model.

关键词： Economics Renewable energy sources Uncertainty Computational modeling Programming Numerical simulation Probabilistic logic

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