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Investigation on the coordinated operation control technology of distributed new energy grid connection using artificial intelligence algorithms

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FRONTIERS IN ENERGY RESEARCH 2023年 11卷

作者： Lu, Yonglin Sun, Jinyong Hu, Jinying Chen, Rui Liao, Yifa Shang, Jingrui Yunnan Power Grid Co Ltd Dehong Power Supply Bur Dehong Yunnan Peoples R China

New energy power generation has strong randomness and volatility. Especially in the case of a high proportion of NE (network security) power generation, its sudden random power generation in a short period of time will seriously affect the stable operation of the power grid. Therefore, this paper proposes BP neural network algorithm to study the distributed NE grid-connected cooperative operation control technology. First of all, this paper studies the artificial intelligence algorithm in detail and applies it to the coordinated operation control of distributed NE grid-connected;then, based on the status quo of renewable energy PG (power generation), this paper establishes a suitable wind speed time series model, and thus proposes an optimization model based on a rolling scheduling optimization algorithm. The experimental results show that the average running time of the rolling scheduling optimization algorithm is maintained at about 0.2 s, which can effectively realize online operation. In addition, through rolling adjustment, the error between the total output curve of the unit and the actual total output curve of the unit can be significantly reduced. The research shows that the rolling scheduling optimization algorithm has a good optimization effect, can promote the coordinated development of wind farms and power systems, and increase the capacity of power systems.

关键词： distributed new energy coordinated operation control technology artificial intelligence algorithms coordinated development

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Indoor Visible Light Positioning System Based on Point Classification Using artificial intelligence algorithms

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SENSORS 2023年第11期23卷

作者： Long, Qianqian Zhang, Junyi Cao, Lu Wang, Wenrui Beijing Univ Posts & Telecommun Sch Elect Engn Beijing 100876 Peoples R China

In RSSI-based indoor visible light positioning systems, when only RSSI is used for trilateral positioning, the receiver height needs to be known to calculate distance. Meanwhile, the positioning accuracy is greatly affected by multi-path effect interference, with the influence of the multi-path effect varying across different areas of the room. If only one single processing is used for positioning, the positioning error in the edge area will increase sharply. In order to address these problems, this paper proposes a new positioning scheme, which uses artificial intelligence algorithms for point classification. Firstly, height estimation is performed according to the received power data structure from different LEDs, which effectively extends the traditional RSSI trilateral positioning from 2D to 3D. The location points in the room are then divided into three categories: ordinary points, edge points and blind points, and corresponding models are used to process different types of points, respectively, to reduce the influence of the multi-path effect. Next, processed received power data are used in the trilateral positioning method for calculating the location point coordinates, and to reduce the room edge corner positioning error, so as to reduce the indoor average positioning error. Finally, a complete system is built in an experimental simulation to verify the effectiveness of the proposed schemes, which are shown to achieve centimeter-level positioning accuracy.

关键词： visible light communication indoor positioning artificial intelligence algorithms point classification

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Optimization of computer programming based on mathematical models of artificial intelligence algorithms

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COMPUTERS & ELECTRICAL ENGINEERING 2023年 110卷

作者： Zheng, Yuhui Zhongyuan Univ Technol Coll Sci Zhengzhou 450007 Henan Peoples R China

artificial intelligence (AI) technology is a remarkable combination of computer science, biotechnology, and other disciplines. However, due to the late start of research on AI algorithms, the lack of practical experience, and an incomplete theoretical system, there are still several issues that need to be solved and improved upon further. This article uses the mathematical model of AI algorithms to optimize computer programing. The importance and practical role of mathematical model-optimization methods and mathematical algorithms based on AI algorithms in computer programing optimization are highlighted. The results show that optimizing computer programing based on mathematical models of AI algorithms can effectively improve the organization and hierarchy of code programing, thus improving the programing efficiency of the code. The application of mathematical models of AI algorithms improved the quality of computer programing, computer programing was optimized, and the utilization of programing space was increased by 5.75%.

关键词： Computer programming optimization Mathematical models artificial intelligence algorithms Computer and electrical engineering Objective function artificial neural networks Distributed system Pattern recognition

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An empirical analysis of applications of artificial intelligence algorithms in wind power technology innovation during 1980-2017

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JOURNAL OF CLEANER PRODUCTION 2021年 297卷

作者： Lee, Mekyung He, Gang Korea Univ Grad Sch Energy & Environm GETPPP Seoul South Korea SUNY Stony Brook Dept Technol & Soc Stony Brook NY 11794 USA

We investigated the applications of artificial intelligence (AI) algorithms in wind power technology changes over time and found that AI accelerates the automation of wind power systems. This study shows evidence of the evolution of wind technology innovation following the advancement in AI algorithms using the patents data issued in four intellectual property (IP) offices from 1980 through 2017. artificial intelligence and more advanced data analytics can be effectively applied to increase the efficiency of wind power systems and to optimize wind farm operations. This study empirically analyzes the evolution of applications of AI algorithms in wind power technology by employing machine learning-based text mining and network analysis, demonstrating the dynamic changing pattern of applications of AI algorithms in wind power technology innovation. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Wind power technology artificial intelligence algorithms Machine learning Technology innovation Patent data analysis

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Interactive Teaching Path Analysis of Higher Vocational Ideological and Political Courses Based on artificial intelligence algorithms 11

Interactive Teaching Path Analysis of Higher Vocational Ideo...

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11th International Conference on Measuring Technology and Mechatronics Automation (ICMTMA)

作者： Zhang Yunfang Wang Yuexi Shandong Inst Commerce & Technol Sch Marxism Jinan 250103 Shandong Peoples R China

ISBN: (纸本)9781728121659

In today's society, science and technology are the primary productive forces, and the reform of any mode can not be separated from science and technology, especially in education and teaching. Most vocational college students lack good learning habits, poor understanding of classroom knowledge, and are not good at solving all kinds of difficulties encountered in learning independently. In order to solve these problems, an interactive teaching path analysis method for ideological and political courses in Higher Vocational Colleges Based on artificial intelligence algorithm is proposed. The objective function of teaching path analysis is established and the constraints are determined. Analyse and use artificial intelligence algorithm to establish interactive teaching path research model. The experimental results show that the interactive teaching path analysis method based on artificial intelligence algorithm has higher practicability than the traditional interactive teaching path research method of Ideological and political course in Higher Vocational colleges, the interactive teaching path of Ideological and political courses in higher vocational colleges can be accurately analyzed.

关键词： artificial intelligence algorithms Research Model Teaching path Higher Vocational Education Objective Function

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Driver Behaviour Prediction Models Using artificial intelligence algorithms And Statistical Modeling

Driver Behaviour Prediction Models Using Artificial Intellig...

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作者： Yangliu Dou McMaster University

学位级别：博士

To improve the safety and comfort of intelligent vehicles, advanced driver models offer promising solutions. However, several shortcomings of these models prevent them from being widely applied in reality. To address these shortcomings, advanced artificial intelligence algorithms in conjunction with the sufficient driving environmental factors are proposed based on real-life driving data. More specifically, three typical problems will be addressed in this thesis: Mandatory Lane Changing (MLC) suggestion at the highway entrance; Discretionary Lane Changing (DLC) intention prediction; Car-Following gap model considering the effect of cuts-in from the adjacent lanes. For the MLC suggestion system, in which the main challenges are efficient decision making and high prediction accuracy of both non-merge and merge events, an additional gated branch neural network (GBNN) is proposed. The proposed GBNN algorithm not only achieves the highest accuracy among conventional binary classifiers in terms of great performance on the non-merge accuracy, the merge accuracy, and receiver operating characteristic score but also takes less time. For the DLC, we propose a recurrent neural network (RNN)-based time series classifier with a gated recurrent units (GRU) architecture to predict the surrounding vehiclesâ intention. It can predict the surrounding vehiclesâ lane changing maneuver 0.8 s in advance at a recall and precision of 99.5% and 98.7%, respectively, which outperforms conventional algorithms such as the Hidden Markov Model (HMM). Finally, drivers are typically faced with two competing challenges when following a preceding vehicle. A method is proposed to address the problem through an overall objective function of car-following gap and velocity. Based on this, seeking the strategic car-following gap translates to finding the optimal solution that minimizes the overall objective function. With the support of field data, the method along with concrete models are instantiate

关键词： DRIVER BEHAVIOUR PREDICTION MODELS artificial intelligence algorithms

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A novel hybrid method of forecasting crude oil prices using complex network science and artificial intelligence algorithms

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APPLIED ENERGY 2018年 220卷 480-495页

作者： Wang, Minggang Zhao, Longfeng Du, Ruijin Wang, Chao Chen, Lin Tian, Lixin Stanley, Eugene Nanjing Normal Univ Sch Math Sci Nanjing 210042 Jiangsu Peoples R China Nanjing Normal Univ Taizhou Coll Dept Math Taizhou 225300 Jiangsu Peoples R China Boston Univ Ctr Polymer Studies Boston MA 02215 USA Boston Univ Dept Phys Boston MA 02215 USA Jiangsu Univ Energy Dev & Environm Protect Strategy Res Ctr Zhenjiang 212013 Jiangsu Peoples R China Beijing Univ Technol Coll Econ & Management Beijing 100124 Peoples R China Northwestern Polytech Univ Sch Management Xian 710072 Shanxi Peoples R China

Forecasting the price of crude oil is a challenging task. To improve this forecasting, this paper proposes a novel hybrid method that uses an integrated data fluctuation network (DFN) and several artificial intelligence (AI) algorithms, named DIN-AI model. In the proposed DFN-AI model, a complex network time series analysis technique is performed as a preprocessor for the original data to extract the fluctuation features and reconstruct the original data, and then an artificial intelligence tool, e.g., BPNN, RBFNN or ELM, is employed to model the reconstructed data and predict the future data. To verify these results we examine the daily, weekly, and monthly price data from the crude oil trading hub in Cushing, Oklahoma. Empirical results demonstrate that the proposed DIN-AI models (i.e., DFN-BP, DEN-RBF, and DFN-ELM) perform significantly better than their corresponding single AI models in both the direction and level of prediction. This confirms the effectiveness of our proposed modeling of the nonlinear patterns hidden in crude oil prices. In addition, our proposed DEN-AI methods are robust and reliable and are unaffected by random sample selection, sample frequency, or breaks in sample structure.

关键词： Complex network artificial intelligence algorithms Crude oil price prediction

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Social entertainment robot based on artificial intelligence algorithm application in interactive mode of music classroom

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ENTERTAINMENT COMPUTING 2024年 51卷

作者： Song, Qianwen Xianyang Normal Univ Sch Mus Xianyang 712000 Shanxi Peoples R China

With the rapid development of artificial intelligence technology, in-depth research on the application of social entertainment robots in the field of education has become increasingly important. Therefore, this study aims to explore how to use social entertainment robots based on artificial intelligence algorithms to improve the interactive mode in music classrooms, in order to enhance learning effectiveness and classroom atmosphere. The study introduced the detection algorithm of social entertainment robots and designed a complete detection process for accurately locating and identifying social entertainment robots in music classrooms. A machine data classification detection algorithm has been proposed to accurately determine the behavior and function of social entertainment robots. The social entertainment robot teaching module guides students to actively participate in music classroom activities through simulated conversations and interactive demonstrations. Interactive tests were conducted in actual music classrooms to evaluate the effectiveness of social entertainment robots in the classroom and student feedback. Through experiments, it has been proven that social entertainment robots based on artificial intelligence algorithms can effectively enhance the interactivity and fun of music classrooms. It not only promotes communication and cooperation among students, but also enhances learning participation.

关键词： artificial intelligence algorithms Social entertainment robots Music classroom Classroom interaction

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artificial intelligence Applied to Drone Control: A State of the Art

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DRONES 2024年第7期8卷

作者： Caballero-Martin, Daniel Lopez-Guede, Jose Manuel Estevez, Julian Grana, Manuel Univ Basque Country UPV EHU Grp Computat Intelligence San Sebastian 20018 Spain Univ Basque Country UPV EHU Fac Engn Alava C-Nieves Cano 12 Vitoria-gasteiz 01006 Spain Univ Basque Country UPV EHU Fac Engn Gipuzkoa Europa Plaza 1 San Sebastian 20018 Spain Univ Basque Country UPV EHU Fac Comp Sci Paseo Manuel de Lardizabal 1 Donostia San Sebastian 20018 Spain

The integration of artificial intelligence (AI) tools and techniques has provided a significant advance in drone technology. Besides the military applications, drones are being increasingly used for logistics and cargo transportation, agriculture, construction, security and surveillance, exploration, and mobile wireless communication. The synergy between drones and AI has led to notable progress in the autonomy of drones, which have become capable of completing complex missions without direct human supervision. This study of the state of the art examines the impact of AI on improving drone autonomous behavior, covering from automation to complex real-time decision making. The paper provides detailed examples of the latest developments and applications. Ethical and regulatory challenges are also considered for the future evolution of this field of research, because drones with AI have the potential to greatly change our socioeconomic landscape.

关键词： artificial intelligence algorithms drones cargo transport autonomous decisions

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Determinants and Pathways for Inclusive Growth in China: Investigation Based on artificial intelligence (AI) Algorithm

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COMPUTATIONAL ECONOMICS 2025年第3期65卷 1231-1264页

作者： Fan, Shuangshuang Li, Yichao Mbanyele, William Lai, Xiufeng Wenzhou Univ Technol Sch Econ & Management Wenzhou 325035 Peoples R China Hainan Univ Sch Informat & Commun Engn Haikou 570228 Peoples R China Shandong Univ Ctr Econ Res Jinan 250100 Peoples R China China Univ Min & Technol Beijing Sch Management Beijing 100091 Peoples R China

Can artificial intelligence (AI) algorithms help policymakers in their decisions on inclusive growth? In this study, we introduce artificial intelligence algorithms to calibrate China's inclusive growth determinants. We uncover various factors that significantly influence inclusive growth using machine learning forecasts. Furthermore, our results using best practice methods outperform findings from traditional regression-based strategies on other dimensions, which miss non-linear interactions in their estimations. However, we observe that when the actual value of the inclusive growth index is too large, the accuracy of the machine learning model is diminished. Meanwhile, the results of heterogeneity analysis reveal that the determinants of inclusive growth in cities with different region and different marketization level are distinct. In addition, we adopt the scenario simulation and prediction approach to reveal the best policy measure to promote inclusive growth in China. Our findings indicate that machine learning holds promise for understanding how inclusive growth can be achieved and can assist real-world economies in enhancing inclusive growth.

关键词： Inclusive growth Machine learning artificial intelligence algorithms Influencing factors China

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