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Empirical Mode Decomposition based multi-objective Deep Belief Network for short-term power load forecasting

NEUROCOMPUTING 2020年 388卷 110-123页

作者： Fan, Chaodong Ding, Changkun Zheng, Jinhua Xiao, Leyi Ai, Zhaoyang Xiangtan Univ Coll Informat Engn Xiangtan 411105 Hunan Peoples R China Fujian Prov Key Lab Data Intens Comp Quanzhou 362000 Fujian Peoples R China Hunan Univ Coll Elect & Informat Engn Changsha 410082 Hunan Peoples R China Hunan Univ Inst Cognit Control & Biophys Linguist CFL Changsha 410082 Hunan Peoples R China

With the rapid development of power grid data, the data generated by the operation of the power system is increasingly complex, and the amount of data increases exponentially. In order to fully exploit and utilize the deep relationship between data to achieve accurate prediction of power load, this paper proposes an Empirical Mode Decomposition Based multi-objective Deep Belief Network prediction method (EMD-MODBN). In the training process of DBN, a multi-objective optimization model is constructed aiming at accuracy and diversity, and MOEA/D is used to optimize the parameters of the model to enhance the generalization ability of the prediction model. Finally, the final load forecasting results are obtained by summing up the weighted outputs of each forecasting model with ensemble learning method. The experimental results show that compared with several current better load forecasting methods, this method has obvious advantages in prediction accuracy and generalization ability. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Empirical Mode Decomposition multi-objective optimization algorithm Ensemble learning Deep belief network Power load forecasting

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A multi-objective Task Scheduling Method for Fog Computing in Cyber-Physical-Social Services

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IEEE ACCESS 2020年 8卷 65085-65095页

作者： Yang, Ming Ma, Hao Wei, Shuang Zeng, You Chen, Yefeng Hu, Yuemei Zhejiang Meteorol Informat Network Ctr Hangzhou 310016 Peoples R China Zhejiang Climate Ctr Hangzhou 310016 Peoples R China Qufu Normal Univ Sch Informat Sci & Technol Rizhao 276800 Peoples R China

Fog computing provides users with data storage, computing, and other services by using fog layer devices close to edge devices. Tasks and resource scheduling in fog computing has become a research hotspot. For the multi-objective task-scheduling problem in fog computing, an adaptive multi-objective optimization task scheduling method for fog computing (FOG-AMOSM) is proposed in this paper. In this method, the total execution time and the task resource cost in the fog network are taken as the optimization target of resource allocation, and a multi-objective task scheduling model is designed. Since the objective model is a Pareto optimal solution problem, the global optimal solution can be obtained by using multi-objective optimization theory and the improved multi-objective evolutionary heuristic algorithm. Moreover, to obtain a better distribution of the current task scheduling group, the neighborhood is adaptively changed according to the current situation of the task scheduling group in fog computing, which avoids the problem that the neighborhood value caused by the neighborhood policy in the multi-objective algorithm affects the distribution of the task scheduling population. This algorithm is used to solve the non-inferior solution set of the utility function index of fog computing task scheduling to try to solve the multi-objective cooperative optimization problem in fog computing task scheduling. The results show that the proposed method has better performance than other methods in terms of total task execution time, resource cost and load dimensions.

关键词： Cloud computing fog computing task scheduling multi-objective optimization algorithm cyber-physical-social service

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multi-objective optimal electric power planning in the power system using Gaussian bare-bones imperialist competitive algorithm

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INFORMATION SCIENCES 2015年 294卷 286-304页

作者： Ghasemi, Mojtaba Ghavidel, Sahand Ghanbarian, Mohammad Mehdi Gitizadeh, Mohsen Shiraz Univ Technol Dept Elect & Elect Engn Shiraz Iran Islamic Azad Univ Kazerun Branch Kazerun Iran

In this paper, a Gaussian Bare-bones multi-objective Imperialist Competitive algorithm (GBICA) and its Modified version (MGBICA) are presented for the optimal electric power planning in the electric power system. Two sub-problems of multi-objective optimal electric power planning namely Optimal Power Flow (OPF) and Optimal Reactive Power Dispatch (ORPD) problems are considered. The OPF and ORPD problems are formulated as a nonlinear constrained multi-objective optimization problem with competing objectives. The performance of multi-objective algorithms are studied and evaluated on the standard IEEE 30-bus and IEEE 57-bus test systems. The proposed algorithm provides better results compared with the other algorithms as demonstrated by simulation results. (C) 2014 Elsevier Inc. All rights reserved.

关键词： multi-objective optimal electric power planning Gaussian bare-bones imperialist competitive algorithm multi-objective optimization algorithm

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Finite element modeling and parameter identification of the seated human body exposed to vertical vibration

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BIOMECHANICS AND MODELING IN MECHANOBIOLOGY 2021年第5期20卷 1789-1803页

作者： Gao, Kaizhan Li, Chunyu Xiao, Yang Zhang, Zhifei Chongqing Univ Sch Automot Engn Chongqing 400030 Peoples R China

Developing a biomechanical model which connected with the actual anatomy of the human body is helpful to understand the human response to vibration. A finite element model of the seated human body with 175 cm in stature and 68.6 kg in weight, which consists of seven segments, six joints and soft tissue, was established to reflect apparent mass based on the Hybrid III dummy model. By comparing the body segment mass percentages with previous data, the rationality of mass distribution in this model was verified. The biomechanical parameters play a crucial role in biodynamic modeling, while the joint and soft tissue parameters are difficult to choose due to the wide range of anthropometric parameters. In this study, the root-mean-square error between the calculated and the measured apparent mass was taken as objective function, and the effect of fifteen human parameters on the objective function was analyzed through sensitivity analysis. Then seven parameters with a considerable influence on the objective function were selected as design variables, and four approximate models were established for parameter optimization. Soft tissues and joint parameters of the model were determined by parameter identification, and the finite element model that can reflect vertical in-line and fore-and-aft cross-axis apparent mass of the human body without backrest was developed. The seated human model presented in this paper can also reflect the transmissibility from seat to the first thoracic spine and the main modes of the human body below 10 Hz, which is conducive to express the human response to vibration.

关键词： Whole-body vibration Finite element modeling Seated human body Apparent mass multi-objective optimization algorithm

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A multi-objective Shuffled Bat algorithm for optimal placement and sizing of multi distributed generations with different load models

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INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS 2016年 79卷 120-131页

作者： Yammani, Chandrasekhar Maheswarapu, Sydulu Matam, Sailaja Kumari Natl Inst Technol Warangal Dept Elect Engn Warangal Telangana India

In this paper a new and efficient hybrid multi-objective optimization algorithm is proposed for optimal placement and sizing of the Distributed generations (DGs) in radial distribution systems. A multi objective Shuffled Bat algorithm is proposed to evaluate the impact of DG placement and sizing for an optimal improvement of the distribution system with different load models. In this study, the ideal sizes and locations of DG units are found by considering the power losses, cost and voltage deviation as objective functions to minimize. Furthermore, the study is verified with voltage dependent load models like industrial, residential, commercial and mixed load models. The feasibility of the proposed technique is verified with the 33 bus distribution network and also the qualitative comparisons against a well-known technique, known as Non-dominated Sorting Genetic algorithm II (NSGA-II) is done and results are presented. (C) 2016 Elsevier Ltd. All rights reserved.

关键词： Distributed generation Power loss minimization multi-objective optimization algorithm Shuffled Bat algorithm Load models

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Dynamic Environmental Economic Dispatch Considering the Uncertainty and Correlation of Photovoltaic-Wind Joint Power

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ENERGIES 2024年第24期17卷

作者： Ru, Yi Wang, Ying Mao, Weijun Zheng, Di Fang, Wenqian China Jiliang Univ Coll Mech & Elect Engn Hangzhou 310018 Peoples R China

The traditional power grid planning lacks consideration of the uncertainty and correlation between wind and solar joint output in the same region, which poses challenges to the stable operation of the power system. Therefore, it is greatly important to consider the environmental and economic dispatch in light of the uncertainties and correlations associated with wind and solar energy. To tackle these issues, this paper introduces a dynamic environmental economic dispatch model that accounts for the uncertainties and correlations between wind and photovoltaic power based on their output characteristics. Initially, a probability model for photovoltaic-wind joint power is established using the Copula function. Subsequently, the Latin hypercube sampling method is employed alongside an improved K-means clustering technique to derive typical output scenarios. An adaptive multi-objective fireworks algorithm, featuring a differential selection strategy, is then utilized to enhance the environmental economic dispatch model. Finally, the IEEE 39 node system is used as an example to demonstrate the solution of the dynamic environmental and economic scheduling model. Simulation results reveal that the method for generating typical output scenarios presented in this paper effectively captures the uncertainties and correlations of photovoltaic-wind joint power. Furthermore, when compared to other optimization algorithms, the improved adaptive multi-objective fireworks algorithm proves to be more efficient in addressing the dynamic environmental economic dispatch challenges within the power system.

关键词： dynamic economic environment scheduling fireworks algorithm multi-objective optimization algorithm photovoltaic-wind joint power uncertainty and correlation

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Optimal allocation of industrial park multi-energy complementary system based on typical scenarios: Case study of Shenzhen

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INTERNATIONAL JOURNAL OF HYDROGEN ENERGY 2024年 88卷 830-840页

作者： Liu, Fangtong Zhong, Jiaqi Wu, Man Liu, Xiaoyang Wang, Chaolang Ke, Yiming Jinan Univ Sch Intelligent Syst Sci & Engn Zhuhai 519070 Peoples R China China Nucl Energy Technol Corp Ltd Beijing 100080 Peoples R China Jinan Univ Int Energy Coll Zhuhai 519070 Peoples R China Jinan Univ Energy & Elect Res Ctr Zhuhai 519070 Peoples R China

The multi-energy complementary system (MECS) is a new mode that converts renewables into electricity and is usually equipped with hydrogen storage. It realizes flexible conversion of electric and hydrogen energy, achieving high efficiency and low carbon. However, how to optimize capacity of each equipment based on operation strategies and objectives becomes the most important issue. Thus, this study focuses on MECS optimal allocation and manages to work out a scientific framework. Firstly, the Density-Based Spatial Clustering of Applications with Noise algorithm and the Calinski Harabasz Index are firstly integrated to improve K-means clustering algorithm and identify typical scenarios. It can efficiently deal with long-sequence scenario reduction under the uncertainty of generation and load demand. Then, different from conventional objective-determining processes, indicators of the net present value, the carbon emission reduction, the power curtailment rate are taken into account to reflect comprehensive benefits from the life cycle of the MECS. Subsequently, for the difficulty of mixed integer fraction optimization, the-constraint method is introduced in model solving. Finally, a case study of Shenzhen is carried out for verification. The proposed models can achieve optimal allocation with different preference, which can provide theoretical and technical reference for MECS development.

关键词： Capacity allocation optimization Wind-PV-Hydrogen complementary system Typical scenario identification multi-objective optimization algorithm

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Fractional techniques to characterize non-solid aluminum electrolytic capacitors for power electronic applications

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NONLINEAR DYNAMICS 2019年第4期98卷 3125-3141页

作者： Chen, Xi Xi, Lei Zhang, Yunning Ma, Hui Huang, Yuehua Chen, Yangquan China Three Gorges Univ Coll Elect Engn & New Energy Yichang 443002 Hubei Peoples R China Univ Calif Merced Sch Engn Merced CA 95343 USA

Non-solid aluminum electrolytic capacitors are one type of reliability-critical components, and they are widely adopted in power electronic converters. The capacitance and equivalent series resistance of these components have significant effects on the performance and reliability of power electronic systems. In this work, by exploring the electrochemical principles of aluminum electrolytic capacitors, the fractional-order (FO) characteristics of the capacitors are revealed, according to which the frequency-dependent parameters of this kind of components are expressed by FO models, while the parameters of the models are estimated by a multi-objective optimization algorithm. Under the same conditions such as the number of arguments supplied and optimization algorithm, the proposed models perform better. Additionally, to show further applications of fractional techniques, a brief example on the output ripple analysis of DC-DC converters is offered, in which one of the proposed FO models of the capacitor is adopted. The effectiveness and superiority of the techniques for predicting the states of the converters are confirmed by comparison with traditional models.

关键词： Aluminum electrolytic capacitors Fractional order (FO) multi-objective optimization algorithm DC-DC converters

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multi-objective optimal offloading decision for multi-user structured tasks in intelligent transportation edge computing scenario

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JOURNAL OF SUPERCOMPUTING 2022年第16期78卷 17797-17825页

作者： Zhu, Sifeng Zhao, Mingyang Zhang, Qinghua Tianjin Chengjian Univ Sch Comp & Informat Engn Tianjin 300384 Peoples R China Tianjin Chengjian Univ Lib Tianjin 300384 Peoples R China

With the continuous increase in the number of vehicles and people on urban roads, various traffic problems have become increasingly serious and intelligent transportation has gradually gained widespread attention. As a computing paradigm that could effectively reduce the task processing time consumption of user device and the cost of cloud server, edge computing has become an indispensable part of intelligent transportation system. However, how to reduce the load imbalance of edge computing server while ensuring that the task processing of intelligent transportation device takes less time consumption and energy consumption has become a challenge. In order to tackle this challenge, the computation offloading decision-making problem in the intelligent transportation edge computing scenario was modeled as a multi-objective optimization problem in this paper, and an adaptive multi-objective optimization algorithm (E-NSGA-III) based on NSGA-III was used to solve this problem, and comparative experiment with other methods was made. Experimental results show that compared with NSGA-II, MOEA/D and NSGA-III, proposed algorithm (E-NSGA-III) in this paper can mostly reduce time consumption by 14.28%, 18.42% and 9.82%, energy consumption by 5.59%, 6.79% and 4.83%, and load balancing variances by 21.73%, 33.46% and 18.25%.

关键词： multi-objective optimization algorithm NSGA-III Edge computing Computation offloading decision Intelligent transportation

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A novel interval forecasting system for uncertainty modeling based on multi-input multi-output theory: A case study on modern wind stations

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RENEWABLE ENERGY 2021年 163卷 88-104页

作者： Liu, Tongxiang Zhao, Qiujun Wang, Jianzhou Gao, Yuyang Univ Adelaide Fac Profess Adelaide SA 5000 Australia Dalian Minzu Univ Int Business Coll Dalian 116600 Peoples R China Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

With the growing demand for a clean energy source, wind power is drawing increasing attention. However, its intermittence and fluctuation set strict restrictions on its development and applications. Although a vast amount of research has been conducted on this subject, studies have failed to charac-terize the uncertainties of the growing intervals and have focus only on point prediction. Therefore, this paper proposes an interval prediction system that can effectively avoid the drawbacks of point forecasting. The system is composed of five units: a preprocessing unit, a feature selection unit, an optimization unit, a forecasting unit, and a result evaluation unit. The preprocessing unit, along with the feature selection unit, is applied to obtain the ideal input data. Then, the forecasting unit, whose key parameters are updated by the optimization unit, is used for interval prediction. The experimental results obtained from various evaluation metrics show that the accuracy of the developed system exceeds that of benchmark methods, and also confirm the possibility of applying the proposed method in the effective utilization of wind energy. (C) 2020 Elsevier Ltd. All rights reserved.

关键词： Interval forecasting Wind speed Data preprocessing Feature selection multi-objective optimization algorithm

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