检索结果-内蒙古大学图书馆

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

Intelligent multivariable air-quality forecasting system based on feature selection and modified evolving interval type-2 quantum fuzzy neural network

引用

ENVIRONMENTAL POLLUTION 2021年 274卷 116429-116429页

作者： Wang, Jianzhou Li, Hongmin Yang, Hufang Wang, Ying Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Owing to the high nonlinearity and noise in the air quality index (AQI), tackling the uncertainties and fuzziness in the forecasting process is still a prevalent problem. Therefore, this study developed an intelligent hybrid air-quality forecasting system based on feature selection and a modified evolving interval type-2 quantum fuzzy neural network (eIT2QFNN), which provides accurate air-quality forecasting information by considering climate influencing factors. The main contributions of this study are as follows. The optimal input structure of the model is determined by the proposed second-stage feature-selection model, which can better extract the influencing variables and remove redundant information. Moreover, a novel multi-objective chaotic Bonobo optimizer algorithm is proposed to improve the eIT2QFNN. The modified eIT2QFNN implements AQI prediction by considering the importance of influencing variables that can cope with the uncertainties and fuzziness in the forecasting process. Finally, the Diebold-Mariano and modified Diebold-Mariano tests are employed to evaluate the performance of the proposed system. The experimental results demonstrate that our proposed system significantly improves the modeling performance in terms of high accuracy and compact structure, and can thus serve as an effective tool for air-quality management. (C) 2021 Elsevier Ltd. All rights reserved.

关键词： Air quality forecasting Feature selection Interval type-2 fuzzy sets Fuzzy neural network multi-objective optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

Axial force identification of space grid structural members using particle swarm optimization method

引用

JOURNAL OF BUILDING ENGINEERING 2020年 32卷

作者： Ding, Beidou Liu, Jinling Huang, Zhenhua Li, Xian Wu, Xiaosuo Cai, Liping China Univ Min & Technol Jiangsu Key Lab Environm Impact & Struct Safety E Xuzhou 221116 Jiangsu Peoples R China Univ North Texas Dept Mech Engn Denton TX 76207 USA

Space grid structures are popularly used in large-span civil structures. Identification of the axial internal forces and boundary conditions of the space grid structural members is very important. However, in the engineering practice, the boundary conditions of such members are not ideally rigid or pinned. They are sometimes semi-rigid conditions. Besides, direct measurements of the axial forces and the boundary rigidities of in-situ space grid structural members are difficult to make. Therefore, in this study, a Particle Swarm optimization (PSO) algorithm-based axial force and boundary rigidity identification method is proposed for space grid structural members using multi-order natural frequencies, which can be easily obtained from in-situ tests. The theoretical background of the proposed method is discussed in detail, including the coupling relationship between the axial force and natural frequency and the PSO algorithm. The applicability and accuracy of the proposed method are validated through experimental tests and comparative analyses.

关键词： Particle swarm optimization (PSO) Natural vibration frequency multi-objective optimization algorithm Axial force identification Grid structure

来源：评论

学校读者我要写书评

暂无评论

A new combined model based on multi-objective salp swarm optimization for wind speed forecasting

引用

APPLIED SOFT COMPUTING 2020年 92卷 106294-106294页

作者： Cheng, Zishu Wang, Jiyang Dongbei Univ Finance & Econ Sch Stat Dalian Peoples R China Macau Univ Sci & Technol Fac Informat Technol Macau 999078 Peoples R China

Wind energy as the representative renewable energy sources attracted the global attention and wind power plays a significant role in power system. Thus, wind speed forecasting is highly critical in wind power grid management. The short-term wind speed prediction can effectively support power grid-management to reduce wind curtailments. In the past, lots of researches had often considered how to enhance the accuracy or stability in short wind speed forecasting. Nevertheless, just focus on one criterion is the inability to build an effective predictive system. In this paper, a novel combined forecasting system was proposed and effectively applied to address the issue of wind speed prediction while obtaining high precision and strong stability simultaneously at the same time. Four ANNs (artificial neural networks) were combined by the optimal weighting coefficients determined by MSSO (multi-objective salp swarm optimizer) in this system and data decomposition and denoising are included in the data preprocessing stage. The multi-objective optimization algorithm overcomes the weakness of the single-objective optimization algorithm that can only achieve one criterion. It can simultaneously optimize accuracy and stability. The 10-minute wind speed data of three data sets of Penglai, China were selected for multi-step forecasting to evaluate the effectiveness of the proposed combined model. And experimental results show that the proposed model not only achieves excellent precision and stability but also outperforms other proposed combined models. (C) 2020 Elsevier B.V. All rights reserved.

关键词： Combined model Wind speed forecasting multi-objective optimization algorithm Data preprocessing technique

来源：评论

学校读者我要写书评

暂无评论

Point and interval prediction for non-ferrous metals based on a hybrid prediction framework

引用

RESOURCES POLICY 2021年 73卷 1页

作者： Wang, Jianzhou Niu, Xinsong Zhang, Linyue Lv, Mengzheng Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

As a bulk product with huge international circulation, non-ferrous metals have frequent and severe price fluctuations, which have attracted great attention from academia and industry. However, the non-ferrous metal price series has strong volatility and nonlinear characteristics, which makes the realization of high-precision forecasts still a difficult and challenging problem. In this paper, a hybrid point prediction system is constructed to achieve high precision point prediction results. Moreover, uncertain forecasts contain more information and can provide market participants with more detailed guidance, but uncertainty forecasting is often ignored in practice. Based on the high precision point prediction system, the uncertainty prediction framework is proposed in this paper. Different distribution functions were used to analyze the distribution characteristics of the data, and the uncertainty prediction at different levels was successfully realized according to point prediction results. To verify prediction performance of the proposed prediction framework, multiple contrast experiments have been carried out using the London Metal Exchange daily future prices of Zinc, Copper and Lead. The empirical results show that the developed prediction framework has better predictive power for non-ferrous metals price prediction.

关键词： Non-ferrous metals Interval prediction Artificial intelligence multi-objective optimization algorithm Distribution function

来源：评论

学校读者我要写书评

暂无评论

A novel two-stage forecasting model based on error factor and ensemble method for multi-step wind power forecasting

引用

APPLIED ENERGY 2019年 238卷 368-383页

作者： Hao, Yan Tian, Chengshi Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Liaoning Peoples R China

With the fast growth of wind power penetration into the electric grid, wind power forecasting plays an increasingly significant role in the secure and economic operation of power systems. Although there have been numerous studies concerning wind power forecasting, most of them have failed to make the best of the information implied in the error value, focused only on simple error correction, adopted a simple ensemble method to aggregate the predictions of each component, and considered improving only forecasting accuracy. Recognizing these issues, a novel two-stage forecasting model based on the error factor, a nonlinear ensemble method and the multi-objective grey wolf optimizer algorithm is proposed for wind power forecasting. More specially, in stage I, the extreme learning machine optimized by the multi-objective grey wolf optimizer is used to forecast the components decomposed by variational mode decomposition, and an error prediction model based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to predict forecast errors;also, a novel nonlinear ensemble method based on the extreme learning machine optimized by the multi-objective grey wolf optimizer is utilized to integrate all the components and forecast error values in stage II. Three real-world wind power datasets collected from Canada and Spain are introduced to demonstrate the forecasting performance of the developed model. The forecasting results reveal that the proposed model is superior to all the other considered models in terms of both accuracy and stability and thus can be a useful tool for wind power forecasting.

关键词： Wind power forecasting Two-stage forecasting model multi-objective optimization algorithm Extreme learning machine

来源：评论

学校读者我要写书评

暂无评论

A novel hybrid model for short-term wind power forecasting

引用

APPLIED SOFT COMPUTING 2019年 80卷 93-106页

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

Wind energy prediction has a significant effect on the planning, economic operation and security maintenance of the wind power system. However, due to the high volatility and intermittency, it is difficult to model and predict wind power series through traditional forecasting approaches. To enhance prediction accuracy, this study developed a hybrid model that incorporates the following stages. First, an improved complete ensemble empirical mode decomposition with adaptive noise technology was applied to decompose the wind energy series for eliminating noise and extracting the main features of original data. Next, to achieve high accurate and stable forecasts, an improved wavelet neural network optimized by optimization methods was built and used to implement wind energy prediction. Finally, hypothesis testing, stability test and four case studies including eighteen comparison models were utilized to test the abilities of prediction models. The experimental results show that the average values of the mean absolute percent errors of the proposed hybrid model are 5.0116% (one-step ahead), 7.7877% (two-step ahead) and 10.6968% (three-step ahead), which are much lower than comparison models. (C) 2019 Elsevier B.V. All rights reserved.

关键词： Hybrid forecasting model Wavelet neural network multi-objective optimization algorithm Wind power forecasting

来源：评论

学校读者我要写书评

暂无评论

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

引用

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

来源：评论

学校读者我要写书评

暂无评论

A Comparative Study on Evolutionary multi-objective optimization algorithms Estimating Surface Duct

引用

SENSORS 2018年第12期18卷 4428-4428页

作者： Liao, Qixiang Sheng, Zheng Shi, Hanqing Zhang, Lei Zhou, Lesong Ge, Wei Long, Zhiyong Natl Univ Def Technol Coll Meteorol & Oceanol Nanjing 211101 Jiangsu Peoples R China

The problem of atmospheric duct inversion is usually solved as a single objective optimization problem. Based on ground-based Global Positioning System (GPS) phase delay and propagation loss, this paper develops a multi-objective method including the effect of source frequency and receiving antenna height. The diversity and convergence of solution sets are evaluated for seven multi-objective evolutionary algorithms with three performance metrics: Hypervolume (HV), Inverted Generational Distance (IGD), and the averaged Hausdorff distance (Delta(2)). The inversion results are compared with the simulation results, and the experimental comparison is conducted on three groups of test situations. The results demonstrate that the ranking of algorithm performance varies because of the different methods used to calculate performance metrics. Moreover, when the algorithms show overwhelming performance using performance metrics, the inversion result is not more close to the real value. In the comparison of computational experiments, it was found that, as the retrieved parameter dimension increases, the inversion result becomes more unstable. When the observed data are sufficient, the inversion result seems to be improved.

关键词： multi-objective optimization algorithm atmospheric duct GPS hypervolume inverted generational distance

来源：评论

学校读者我要写书评

暂无评论

Modelling of carbon price in two real carbon trading markets

引用

JOURNAL OF CLEANER PRODUCTION 2020年 244卷

作者： Hao, Yan Tian, Chengshi Wu, Chunying Dongbei Univ Finance & Econ Sch Stat Dalian 116025 Peoples R China

Carbon price forecasting plays a vital role in establishing a reasonable and stable carbon market. A number of carbon price forecasting models have been developed to improve the effectiveness of the predictions. However, most of the previous studies failed to focus on the role of choosing the appropriate input features and only aimed to improve the forecasting accuracy. In this paper, a novel hybrid model based on feature selection and a multi-objective optimization algorithm is proposed for carbon price forecasting. More specifically, the main novel contributions of this study are as follows. A two-stage feature selection method is developed to obtain the appropriate input variables to enhance the forecasting ability. In addition, the weighted regularized extreme learning machine is optimized using a multi-objective optimization algorithm, named the multi-objective grasshopper optimization algorithm, which can obtain better forecasting results. To demonstrate the effectiveness of the developed carbon price forecasting model, two daily carbon price datasets that were collected from the China and European Union Emissions Trading Scheme, are used in this study. The results revealed that the mean absolute percentage errors of the proposed model utilizing data from the China and European Union Emissions Trading Scheme are 2.4923% and 0.8418%, respectively, which are lower than those of other compared models. In addition, the variances of the forecasting errors of the developed model are 1.1419 and 0.0038 for the data from the China and European Union Emissions Trading Scheme, respectively. These results reflect the superior forecasting ability of this method compared to other methods. Therefore, the proposed method is more effective than other models in carbon price forecasting. (C) 2019 Elsevier Ltd. All rights reserved.

关键词： Carbon price forecasting Hybrid model Two-stage feature selection multi-objective optimization algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：