2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)最新文献

Evolving iterative methods for simultaneous identification and control 同时辨识与控制的演化迭代方法

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502368

L. Keviczky, C. Bányász

引用次数: 0

A hybrid evolutionary probabilistic forecasting model applied for rainfall and wind power forecast 应用于降雨和风力预报的混合进化概率预测模型

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502494

Guilherme G. Netto, Alexandre C. Barbosa, Mateus N. Coelho, Arthur R. L. Miranda, V. N. Coelho, M. Souza, F. Guimarães, Agnaldo J. R. Reis

{"title":"A hybrid evolutionary probabilistic forecasting model applied for rainfall and wind power forecast","authors":"Guilherme G. Netto, Alexandre C. Barbosa, Mateus N. Coelho, Arthur R. L. Miranda, V. N. Coelho, M. Souza, F. Guimarães, Agnaldo J. R. Reis","doi":"10.1109/EAIS.2016.7502494","DOIUrl":"https://doi.org/10.1109/EAIS.2016.7502494","url":null,"abstract":"Several works in the literature so far have been focused on deterministic point forecasts, which, usually, indicates the conditional mean of future observations. An increasing need for generating the entire conditional distribution of future observations has been required for the new generation of soft sensors. This study aims the probabilistic forecasts, reporting the use of a hybrid fuzzy forecasting model applied in two different forecasting problems. Our adapted model is applied to predict the rain of the city of Vitoria, in the state of Espírito Santo, Brazil. Real data from a wind farm, provided by the Irish EirGrid institute, was used for analyzing the proposal over a real time series with high fluctuations. Due to the stochasticity of the the hybrid model, which is calibrated through the use of an evolutionary metaheuristic, we adapted it in order to generate future using quantile regression. Computational experiments indicated the ability of the model in finding useful probabilistic quantiles, which were flexible enough in order to limit the lower and upper bounds of the historical datasets. While the probabilistic quantiles suggested the probability of rain and its magnitude, they were also able to predict expected ranges of the amount of energy generated from the wind farm.","PeriodicalId":303392,"journal":{"name":"2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132328945","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Efficient SQL adaptive query processing in cloud databases systems 云数据库系统中高效的SQL自适应查询处理

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502501

C. Costa, C. Leite, A. Sousa

引用次数: 2

Achieving semi-supervised incremental learning with Learn++ and simple recycled selection 利用lear++和简单的循环选择实现半监督式增量学习

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502504

F. D. Bortoloti, P. M. Ciarelli

引用次数: 2

Fuzzy controller in the cargo control wagons dump 模糊控制器在货车倾卸货物控制中的应用

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502365

J. P. Moura, J. V. F. Neto

引用次数: 4

MPC, objective function with economic cost MPC，具有经济成本的目标函数

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502507

Abel F. Alves, H. F. S. Freitas, C. Andrade, M. Ravagnani

{"title":"MPC, objective function with economic cost","authors":"Abel F. Alves, H. F. S. Freitas, C. Andrade, M. Ravagnani","doi":"10.1109/EAIS.2016.7502507","DOIUrl":"https://doi.org/10.1109/EAIS.2016.7502507","url":null,"abstract":"The distillation is a common method with great energy expenditure used for separation in the oil, food, chemical, etc. However, the technology currently used today in the distillation process is not very different from that used in the first distillation columns in the 19th century. The requirements of thermal energy in the distillation process are enormous. The thermodynamic efficiency of the distillation process is less than 10%. It is estimated that 8% of all energy used by U.S. industries is consumed in the distillation process. Energy is responsible for 50 to 60% of the operating costs of refineries while in chemical that proportion varies from 30 to 40%. These data shows the potential of savings that the distillation process can be achieved when the process is subjected to better control and optimized. The Model Based Predictive Control (MPC) is an advanced control technique with features that solve operational problems present in distillation columns. The MPC can deal with of multivariable systems with interactions and considerable dead times, nonlinearities and restrictions on the variables. One of the most important steps in the MPC is the minimization of an objective function. Different types of objective functions can be used in the MPC control algorithm with specific parameter settings for each type of objective function. In this work, the Wood-Berry model for distillation columns will be used. One MPC control strategy for column using a objective function with economic cost will be implemented. Finally, they will be made adjustments to the parameters of the objective function in order to see how these settings influence the response of the MPC controller.","PeriodicalId":303392,"journal":{"name":"2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"171 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115417948","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1

Supplementary decision system for messages coming from the interpretation of anomalies in time series 补充决策系统对来自时间序列异常的信息进行解释

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502370

S. Nicoli, R. Lins, J. Jardini

引用次数: 0

Evolving fuzzy model based performance identification for production control 基于演化模糊模型的生产控制性能识别

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502496

G. Andonovski, G. Mušič, S. Blažič, I. Škrjanc

引用次数: 5

Evolving fuzzy clustering algorithm based on maximum likelihood with participatory learning 基于参与式学习的最大似然进化模糊聚类算法

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502493

Orlando Donato Rocha Filho, G. Serra

引用次数: 5

Application of Multi-objective Evolutionary Algorithms in automatic restoration of radial power distribution systems 多目标进化算法在径向配电系统自动恢复中的应用

2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS) Pub Date : 2016-05-23 DOI: 10.1109/EAIS.2016.7502369

Nestor Rocha Monte Fontenele, L. S. Melo, R. Leão, R. F. Sampaio

{"title":"Application of Multi-objective Evolutionary Algorithms in automatic restoration of radial power distribution systems","authors":"Nestor Rocha Monte Fontenele, L. S. Melo, R. Leão, R. F. Sampaio","doi":"10.1109/EAIS.2016.7502369","DOIUrl":"https://doi.org/10.1109/EAIS.2016.7502369","url":null,"abstract":"When a permanent fault occurs in a power distribution system, the network can be reconfigured in order to restore the supply of some loads situated on non-faulty paths. This paper presents an algorithm developed in Python for optimize the automatic reconfiguration and restoration of radial power distribution systems after the occurrence of a permanent fault. It uses the Multi-objective Evolutionary Algorithm technique and the Step Method in order to optimize all objectives of a given problem, thus providing a greater number of possible solutions. The goals set to the multi-objective function are the maximization of restored customers, minimization of Joule losses and the number of switching maneuvers in the network for the restoration, which are subject to operational constraints. The software features a set of non-dominated solutions, providing the operator with the option to choose from several effective configurations. The grid is modeled by using the node-depth representation (NDR), and the operating constraints evaluated by the forward / backward sweep load flow method. The 16-bus IEEE test system and a proposed 41-bus test system are used to analyze the response of the developed application, which presents good performance and can be safely used by radial distribution system operators.","PeriodicalId":303392,"journal":{"name":"2016 IEEE Conference on Evolving and Adaptive Intelligent Systems (EAIS)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115694864","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 6