As a potential new energy power generation technology, wind power is gradually developing into the world's mainstream energy. In the research on wind power generation, wind speed prediction is an important part, w...
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As a potential new energy power generation technology, wind power is gradually developing into the world's mainstream energy. In the research on wind power generation, wind speed prediction is an important part, which has been widely studied. The accurate wind speed prediction is a key part of wind power management to help wind power grid-tied. Currently, most research has focused on point prediction, which in fact does not facilitate the quantitative characterization of the endogenous uncertainty involved. However, interval prediction can avoid this deficiency and make better operation and scheduling of the wind power models. In this study, a novel interval prediction model based on wind speed distribution and multi-objectiveoptimization is designed, which includes data noise reduction module, prediction module, and combination module based on a multi-objective salp swarm algorithm, to provide accurate forecast for power model operation and grid dispatching. The 10-minute wind speed data from three data sets in China were selected for prediction to evaluate the effectiveness of the proposed combined model. The results show that the model is not only better than the considered benchmark model, but also has good potential practical application value in wind power models. (C) 2021 Elsevier B.V. All rights reserved.
A finite set of best trade-off solutions, or Pareto frontier, is the searching result of a multi-objective optimization algorithm against a multi-objectiveoptimization problem. However, not all the solutions of the s...
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ISBN:
(纸本)9781450376785
A finite set of best trade-off solutions, or Pareto frontier, is the searching result of a multi-objective optimization algorithm against a multi-objectiveoptimization problem. However, not all the solutions of the set are equally important to decision makers. They typically utilize only a few outstanding solutions, and the solutions located at the maximum convex bulge on the Pareto frontier are usually embraced when no preference is available;they are called knee solutions. There are several knee searching algorithms in the last decades, but most of them failed to isolate the knee solutions from the near knee solutions. In this paper, we propose a posteriori knee searching algorithm that can identify and isolate the knee solutions, based on the farthest distance to a hyperplane among the neighborhood solutions. The proposed algorithm is tested against well-known benchmark problems: ZDT3, DEB2DK and DEB3DK. The results show that the proposed algorithm can identify outstanding solutions which are knee solutions accurately.
The cable-stiffened latticed shell is a hybrid structure composed of a single-layer latticed shell and a cable-strut system. Only after the initial prestress is applied can the rigid and flexible components work toget...
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The cable-stiffened latticed shell is a hybrid structure composed of a single-layer latticed shell and a cable-strut system. Only after the initial prestress is applied can the rigid and flexible components work together, so this paper proposes a new initial prestress design and optimization method of cable-stiffened latticed shells. Firstly, based on the equilibrium matrix theory and static analysis, independent self-internal force modes of the hybrid structure are obtained accurately. The force finding analysis is carried out by a linear combination of these self internal force modes, ensuring the initial cable forces in computational models are the same as the design values. Then the design values of the initial forces in a cable-stiffened latticed shell are optimized, taking the combination coefficients of the self-internal force modes as the optimization variables. And the maximum bending moment of the key members and the critical load coefficient when the emergence of slacking cables are considered as optimizationobjectives. Then, a hybrid multi-objective optimization algorithm is proposed based on the interior point method and NGSA-II algorithm. The optimization example shows that the initial prestress optimization has a significant effect on the stability of the structure. The maximum bending moment can be reduced by 18%, and the ultimate bearing capacity can be increased by 47%, which verifies the correctness and applicability of the proposed design and optimization method. (c) 2021 Elsevier Ltd. All rights reserved.
The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order t...
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The rational design of weighting factors in the cost function for finite control set model predictive torque control(FCS-MPTC) has been a matter of great interest in power electronics and electrical drives. In order to solve this problem, a weighting factors autotuning strategy for FCS-MPTC of permanent magnet synchronous motor(PMSM) based on the adaptive multi-objective black hole algorithm(AMOBH) is proposed. In this paper, the design process of the FCS-MPTC algorithm is first analyzed in detail. Then, an AMOBH algorithm that can take into account both population convergence and population diversity is introduced, and based on this algorithm, the design problem of the weighting factors is successfully transformed into a multiobjectiveoptimization problem by means of reconstructing the cost function and designing the motor operation information collected in real time as the objective functions of the multi-objective optimization algorithm. Simulation results show that the proposed method can find a set of weighting factor combinations suitable for different working condition requirements, and these weighting factors can effectively improve the operation performance of the PMSM system.
For solving multi-objectiveoptimization problems, this paper firstly combines a multi-objective evolutionary algorithm based on decomposition (MOEA/D) with good convergence and non-dominated sorting genetic algorithm...
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ISBN:
(纸本)9781728160429
For solving multi-objectiveoptimization problems, this paper firstly combines a multi-objective evolutionary algorithm based on decomposition (MOEA/D) with good convergence and non-dominated sorting genetic algorithm II (NSGA-II) with good distribution to construct. Thus we propose a hybrid multi-objectiveoptimization solving algorithm. Then, we consider that the population diversity needs to be improved while applying multi-objective particle swarm optimization (MOPSO) to solve the multi-objectiveoptimization problems and an improved MOPSO algorithm is proposed. We give the distance function between the individual and the population, and the individual with the largest distance is selected as the global optimal individual to maintain population diversity. Finally, the simulation experiments are performed on the ZDT\DTLZ test functions and track planning problems. The results indicate the better performance of the improved algorithms.
作者:
Li, ChenLeiden Univ
Inst Environm Sci CML POB 9518 NL-2300 RA Leiden Netherlands
The transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which provides a cleaner energy source to the urban smart grid system. However, owing to the volatility and intermi...
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The transition of the energy system from fossil fuel towards renewable energy (RE) is rising sharply, which provides a cleaner energy source to the urban smart grid system. However, owing to the volatility and intermittency of RE, it is challenging to design an accurate and reliable short-term load forecasting model. Recently, machine learning (ML) based forecasting models have been applied for short-term load forecasting whereas most of them ignore the importance of characteristics mining, parameters fine-tuning, and forecasting stability. To dissolve the above issues, a short-term load forecasting model is proposed that incorporates thorough data mining and multi-step rolling forecasting. To alleviate the chaos of short-term load, a de-noising method based on decomposition and reconstruction is used. Then, a phase space reconstruction (PSR) method is employed to dynamically determine the train-test ratios and neurons settings of the artificial neural network (ANN). Further, a multi-objective grasshopper optimizationalgorithm (MOGOA) is applied to optimize the parameters of ANNs. Case studies are conducted in the urban smart grid systems of Victoria and New South Wales in Australia. Simulation results show that the proposed model can forecast short-term load well with various measurement metrics. multiple criterion and statistical evaluation also show the good performance of the proposed forecasting model in terms of accuracy and stability. To conclude, the proposed model achieves high accuracy and robustness, which will provide references to RE transitions and smart grid optimization, and offer guidance to sustainable city development.
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 mul...
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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-objectiveoptimization 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-objectiveoptimization 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-objectivealgorithm 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.
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...
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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-objectiveoptimization 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.
The air quality index (AQI) can reflect the change of air quality in real time. It has linear characteristics, nonlinear and fuzzy features. However, a single model cannot fit the dynamic changes of AQI scientifically...
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The air quality index (AQI) can reflect the change of air quality in real time. It has linear characteristics, nonlinear and fuzzy features. However, a single model cannot fit the dynamic changes of AQI scientifically and reasonably. Therefore, this paper proposes a new dynamic ensemble forecasting system based on multi-objective intelligent optimizationalgorithm to forecast AQI, which has time-varying parameter weights and mainly contains three module: data preprocessing module, dynamic integration forecasting module and system evaluation module. In the data preprocessing module, the off-line frequency domain filtering approach is applied to identify and correct the outliers in the series. To better extract the series information and remove the random noise, the time series is decomposed into multi-level utilizing decomposition strategy and reconstructed. In the dynamic integration forecasting module, three hybrid models based on ARIMA, optimized extreme learning machine and fuzzy time series model, named as HCA, HCME and HCFL respectively, are used to forecast the reconstructed series and time varying parameters are employed to dynamically combine the forecasting results. In the system evaluation module, the accuracy of the system was tested by parameter test method and non-parametric test method respectively. The results demonstrate that the proposed dynamic integrated model is not only superior to other comparison models in forecasting accuracy, but also provides strong technical support for air quality forecasting and treatment.
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 war...
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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.
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