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Research and application of the hybrid forecasting model based on secondary denoising and multi-objective optimization for air pollution early warning system

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JOURNAL OF CLEANER PRODUCTION 2019年 234卷 54-70页

作者： Wang, Jianzhou Bai, Lu Wang, Siqi Wang, Chen Dongbei Univ Finance & Econ Sch Stat Dalian 116023 Peoples R China Lanzhou Univ Sch Math & Stat Lanzhou 730000 Gansu Peoples R China

With the increasing irreversible damage caused by air pollution, an early warning system to send warning information to human beings so that they can avoid more harm caused by air pollution is required. A reliable warning system can provide valuable information to protect mankind from the effects of pollution and can act as a tool that allows regulators to implement corresponding measures to reduce air pollution. However, the previous most valuable research studies were focused on pollution forecasting and the extent to which pollution affects health, and the aim of only a few studies was to analyze pollution from an application perspective and to construct a reasonable early warning system. In this study, an air pollution early warning system was constructed, which comprises two modules: an air pollution forecasting module and an air quality evaluation module. In the forecasting module, two denoising methods and a multi-objective optimization algorithm are integrated into a novel hybrid forecasting model. In the evaluation module, fuzzy synthetic evaluation is used to evaluate air quality objectively. To verify the performance of the proposed early warning system, hourly pollutants concentration data were used in a case study of three metropolises in China and three numeric simulation experiments were conducted. The simulation results show that the forecasting performance of the L-2,L-1 RF-ELM model used in this study is better than the traditional neural network, and the forecasting model proposed in this paper is better than the traditional statistical model ARIMA. Moreover,the early warning system performed well in terms of highly accurate forecasting and accurate evaluation in the three research areas. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Early warning system Air pollution forecasting Secondary denoising multi-objective optimization algorithm Fuzzy synthetic evaluation

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A newly combination model based on data denoising strategy and advanced optimization algorithm for short-term wind speed prediction

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JOURNAL OF AMBIENT INTELLIGENCE AND HUMANIZED COMPUTING 2022年第7期14卷 8271-8290页

作者： Lv, Mengzheng Wang, Jianzhou Niu, Xinsong Lu, Haiyan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Fac Engn & Informat Technol Sch Software Sydney NSW Australia

Accurate implementation of short-term wind speed prediction can not only improve the efficiency of wind power generation, but also relieve the pressure on the power system and improve the stability of the grid. As is known to all, the existing wind speed prediction systems can improve the performance of the prediction in some sense, but at the same time they have some inherent shortcomings, just like forecasting accuracy is not high or indicators are difficult to obtain. In this paper, based on 10-min wind speed data from a wind farm, a new combination model is developed, which consists of three parts: data noise reduction techniques, five artificial single-model prediction algorithms, and multi-objective optimization algorithms. Through detailed and complete experiments and tests, the results demonstrate that the combination model has better performance than other models, solving the problem of instability of traditional forecasting models and filling the gap of low-prediction short-term wind speed forecasting.

关键词： Short-term wind speed forecasting Combination model Data denoising technique multi-objective optimization algorithm

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Incremental Learning Strategy-Assisted multi-objective optimization for an Oil-Water Mixed Cooling Motor

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JOURNAL OF THERMAL SCIENCE AND ENGINEERING APPLICATIONS 2023年第12期15卷 121004页

作者： Li, Wei Li, Yongsheng Li, Congbo Wang, Ningbo Fu, Jiadong Hefei Univ Technol Sch Mech Engn Hefei 230009 Peoples R China Huazhong Univ Sci & Technol Sch Civil & Hydraul Engn Wuhan 430074 Peoples R China Chongqing Univ Coll Mech & Vehicle Engn Chongqing 400044 Peoples R China Chongqing Jinkang Epowertrain Co Ltd Chongqing 401335 Peoples R China

As the core component of electric vehicles (EVs), the performance of motors affects the use of EVs. Motors are sensitive to temperature, and overheated operating temperature may cause the deterioration of the magnetic properties and the reduction of efficiency. To effectively improve the heat dissipation of the motor, this work presents an incremental learning strategy-assisted multi-objective optimization method for an oil-water mixed cooling induction motor (IM). The key parameters of the motor are modeled parametrically, and the design of the experiment is carried out by the Latin hypercube method. The incremental learning strategy is used to improve the low accuracy of the surrogate model. Four multi-objective optimization algorithms are used to drive the optimization process, and the optimal cooling system parameters are obtained. The reliability of the proposed method is verified by motor bench experiments. The optimization results suggest that the maximum temperature of the motor is reduced by 5 K after optimization, and the heat dissipation of the motor is improved effectively, which provides a theoretical basis for further promotion and improvement of the induction motor.

关键词： electric vehicles induction motor oil-water mixed cooling surrogate model incremental learning strategy multi-objective optimization algorithm energy efficiency thermal systems

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A multi-objective optimization-Based Reflective Metasurface for Enhanced multi-Point Focusing With Diffraction Suppression

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RADIO SCIENCE 2024年第7期59卷 1-12页

作者： Xiao, Dongping Xu, Lanxin Su, Dongping Shi, Zhuxin Zhang, Huaiqing Chongqing Univ Sch Elect Engn Natl Key Lab Power Transmiss Equipment Technol Chongqing Peoples R China

Metasurface arrays can achieve beam control at low cost and high quality by providing different phase compensations for each unit, effectively focusing microwave energy on target locations. With the development of short-range communication technology or microwave power transmission technology, the demand for focusing has also increased. Using metasurface arrays to achieve multi-target focusing has wide application value. However, as the number of focal points increases, the superposition of electromagnetic wave propagation paths leads to significant interference phenomena, which can impact potential applications. Existing solutions are unable to solve such complex problems involving a large number of targets with conflicts between them. multi-objective algorithms, by iteratively obtaining a set of optimal solutions, provide decision support for designers in complex multi-objective problems. This paper alters the phase of cells in a reflective array, calculates the near-field electric field model using the Fresnel diffraction formula, and employs various solutions using the Non-dominated Sorting Genetic algorithm III (NSGA-III) combined with different constraints. Finally, we select the balanced solution to establish the array. After simulation, three adjacent focal points with normalized central values of 1, 0.86, and 0.88 were obtained, with the maximum electric field value outside the focal points being only 0.58, demonstrating the feasibility of multi-objective algorithms in solving complex multi-focal problems. Near field multi-objective focusing is the concentration of microwave energy on multiple target locations within the near-field range multiple-objective algorithms is a computational methods for multi-objective optimization problems and provides a set of optimal solutions

关键词： multi-point focusing reflective metasurface multi-objective optimization algorithm near field

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Novel analysis-forecast system based on multi-objective optimization for air quality index

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JOURNAL OF CLEANER PRODUCTION 2019年 208卷 1365-1383页

作者： Li, Hongmin Wang, Jianzhou Li, Ranran Lu, Haiyan Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China Univ Technol Sch Software Fac Engn & Informat Technol Sydney NSW Australia

The air quality index (AQI) is an important indicator of air quality. Owing to the randomness and non-stationarity inherent in AQI, it is still a challenging task to establish a reasonable analysis forecast system for AQI. Previous studies primarily focused on enhancing either forecasting accuracy or stability and failed to improve both aspects simultaneously, leading to unsatisfactory results. In this study, a novel analysis forecast system is proposed that consists of complexity analysis, data preprocessing, and optimize forecast modules and addresses the problems of air quality monitoring. The proposed system performs a complexity analysis of the original series based on sample entropy and data preprocessing using a novel feature selection model that integrates a decomposition technique and an optimization algorithm for removing noise and selecting the optimal input structure, and then forecasts hourly AQI series by utilizing a modified least squares support vector machine optimized by a multi-objective multi verse optimization algorithm. Experiments based on datasets from eight major cities in China demonstrated that the proposed system can simultaneously obtain high accuracy and strong stability and is thus efficient and reliable for air quality monitoring. (C) 2018 Elsevier Ltd. All rights reserved.

关键词： Air quality index forecasting Analysis-forecast system Feature selection multi-objective optimization algorithm Forecasting accuracy and stability

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The optimization of Supply-Demand Balance Dispatching and Economic Benefit Improvement in a multi-Energy Virtual Power Plant within the Jiangxi Power Market

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ENERGIES 2024年第18期17卷

作者： Xinfa, Tang Jingjing, Wang Yonghua, Wang Youwei, Wan Jiangxi Sci & Technol Normal Univ Sch Econ Management & Law Nanchang 330038 Peoples R China State Grid Jiangxi Elect Power Co Ltd Nanchang 330032 Jiangxi Peoples R China

This paper presents an optimization method for scheduling a multi-energy VPP (Virtual Power Plant) supply-demand balance in the power market environment of Jiangxi Province. The primary objective of this method is to improve the operational efficiency of the power grid, reduce energy costs, and facilitate economical and efficient energy distribution in the power market. The method takes into account the characteristics and uncertainties of renewable energy sources such as solar and wind energy, and incorporates advanced multi-objective optimization algorithms. Furthermore, it integrates real-time market price feedback to achieve the accurate allocation of power supply and demand. Through a case study of a multi-energy VPP in Jiangxi Province, this paper examines the optimal combination model for various energy sources within VPP, and analyzes the impact of different market environments on supply-demand balance. The results demonstrate that the proposed scheduling optimization method significantly improves economic benefits while ensuring grid stability. Compared with traditional power supply models, it reduces average electricity costs by 15% and increases renewable energy utilization efficiency by 20%.

关键词： multi-energy VPP balance between supply and demand scheduling planning optimization multi-objective optimization algorithm energy efficiency of renewable energy

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A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting

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APPLIED ENERGY 2019年 235卷 1205-1225页

作者： Yang, Wendong Wang, Jianzhou Niu, Tong Du, Pei Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Liaoning Peoples R China

Electricity price forecasting plays a crucial role in balancing electricity generation and consumption, which is of great political and economic significance for all of society but is still a challenging task. However, in previous studies, most researchers have focused on improving either forecasting accuracy or stability while ignoring the significance of performing these tasks simultaneously. More importantly, few researchers have deeply studied the data preprocessing strategy, only focusing on the application of individual decomposition approaches. Therefore, a novel hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization is developed for electricity price forecasting that includes four modules: a data preprocessing module, optimization module, forecasting module and evaluation module. In this system, an effective multi-objective optimization algorithm is employed to guarantee simultaneous improvements in accuracy and stability. In addition, an improved data preprocessing approach named the dual decomposition strategy is developed, which successfully overcomes the potential drawback of the individual decomposition approach and further improves the effectiveness of the developed forecasting system. Moreover, the evaluation module is incorporated to verify the superiority of the developed forecasting system. Case studies utilizing half-hourly electricity price data collected from New South Wales, Australia are employed as examples. The results prove the superiority of the multi-objective optimization algorithm and the developed dual decomposition strategy and reveal that the developed forecasting system outperforms all of the considered comparison models, which shows its better ability to forecast future electricity prices with better accuracy and stability.

关键词： Electricity price forecasting system Dual decomposition strategy multi-objective optimization algorithm Forecasting accuracy and stability

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Comparative research of optimization algorithms for parameters calibration of watershed hydrological model

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JOURNAL OF COMPUTATIONAL METHODS IN SCIENCES AND ENGINEERING 2016年第3期16卷 653-669页

作者： Huo, Jiuyuan Liu, Liqun Zhang, Yaonan Lanzhou Jiaotong Univ Sch Elect & Informat Engn Lanzhou 730070 Gansu Peoples R China Gansu Agr Univ Coll Informat Sci & Technol Lanzhou Gansu Peoples R China Chinese Acad Sci Cold & Arid Reg Environm & Engn Res Inst Lanzhou Gansu Peoples R China Gansu Data Engn & Technol Res Ctr Resources & Env Lanzhou Gansu Peoples R China

Parameter optimization and calibration of the hydrological model has been one of the important research fields in hydrological forecasting. This paper is written to address the inherent defects that traditional parameter optimization of Xinanjiang hydrological model with a single objective entails. These methods cannot fully exploit hydrological characteristics information from hydrological observation. We selected the Nash Sutcliffe coefficient, which is known to be biased for high flows and the logarithmic form of the Nash Sutcliffe coefficient that emphasize low-flow values as the objective functions. Then, we adopted the multi-objective optimization algorithms, such as the Nondominated Sorted Genetic algorithm-II (NSGAII) and the Third Evolution Step of Generalized Differential Evolution (GDE3), and the single-objective optimization algorithm, Simulated Annealing (SA). These algorithms were applied in Heihe River Basin to calibrate parameters of the Xinanjiang hydrological model for long-term prediction of river discharges. Through the evaluation of the Pareto optimal parameter set derived from multi-objective optimization algorithms and the optimal solution obtained from the single objective algorithm, the results showed that the multi-objective optimization algorithms, in particular the NSGA-II algorithm, perform best to locate the Pareto optimal solutions in the parameter search space. They can also obtain better results with respect to the model parameters calibrated by the single objective algorithm. The major contribution of this work is the comparative application research of single-objective optimization with the multi-objective optimization algorithms for the parameters optimization of the Xinanjiang model in the Heihe River basin.

关键词： Hydrological model parameter calibration multi-objective optimization algorithm Xinanjiang model NSGAII algorithm GDE3 algorithm SA algorithm

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Active-Isolation Improvement of Shared-Aperture Beam-Scanning Array Based on Excitation optimization Techniques

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IEEE ANTENNAS AND WIRELESS PROPAGATION LETTERS 2024年第2期23卷 808-812页

作者： Hou, Hui Ying Cheng, Yu Jian Univ Elect Sci & Technol China Sch Elect Sci & Engn EHF Key Lab Fundamental Sci Chengdu 611731 Peoples R China

In this letter, a methodology based on the excitation optimization technique is proposed to increase the array isolation in a shared-aperture beam-scanning array. A multiple-constraint active isolation model is proposed to comprehensively reflect the isolation between the transmitting and receiving arrays. The first constraint condition in such a model ensures that the receiving channels are not this letter, a oversaturated by the energy coupling from the transmitting array. The second constraint condition ensures that the receiving signal is large enough to suppress noise interference. By only optimizing the excitation of array elements while preserving the radiation performance, the active cancellation can be implemented to improve the hybrid active isolation. To validate this method, a K/Ka dual-band dual-circular-polarized shared-aperture beam-scanning array is designed to achieve the active isolation better than 50 dB throughout the scanning range of +/- 50(degrees).

关键词： High isolation multi-objective optimization algorithm transmitting and receiving shared-aperture beam-scanning array

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Design and Implementation of Fuzzy-PI Controllers for PMSM Based on multi-objective optimization algorithms

Design and Implementation of Fuzzy-PI Controllers for PMSM B...

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2017 5th International Conference on Mechanical, Automotive and Materials Engineering（CMAME 2017）

作者： I-Hsi Kao Kuna-Chung Lu Jau-Woei Perng Department of Mechanical and Electro-Mechanical Engineering National Sun Yat-Sen University

Various intelligent algorithms have been applied to our daily lives,such as fuzzy theory,neural networks,and machine *** methods are widely used for solving many real-world problems;however,these algorithms also exhibit deficiencies and *** paper introduces the recently improved algorithm,known as multi-objective particle swarm optimization,based on decomposition and dominance（D^2 MOPSO） in order to design the permanent magnet synchronous motor（PMSM） fuzzy controller for different *** means that the user can easily change the customized controller,according to their ***,this paper compares the final decision of the controller parameter with other algorithms:the multiobjective particle swarm optimization with crowding distance（MOPSO-CD）,and nondominated sorting genetic algorithm II（NSGA-II）.The simulation results of the three algorithms indicate the optimum PMSM controller parameter in the computing software ***,we implement the fuzzy controller in an embedded system（DSP28069） to demonstrate that our design matches the reality system response and meets the user’s demands with ease.

关键词： fuzzy control multi-objective optimization algorithm multi-objective particle swarm optimization MOPSO Nondominated sorting genetic algorithm NSGA-Ⅱ

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