STLF in the User-Side for an iEMS based on Evolutionary training of Adaptive Networks

被引：0
|
作者：
Cardenas, J. J. [1 ]
Giacometto, F. [1 ]
Garcia, A. [1 ]
Romeral, J. L. [1 ]
机构：
[1] Univ Politecn Cataluna, MCIA Grp, Barcelona, Spain
来源：
2012 IEEE 17TH CONFERENCE ON EMERGING TECHNOLOGIES & FACTORY AUTOMATION (ETFA) | 2012年
关键词：
NEURAL-NETWORK; LOAD FORECASTER;
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
It is a fact that the short-term load forecasting (STLF) in the user side is growing interest. Consequently, intelligent energy management systems (iEMSs) are including this capability in order to take autonomous decisions. In this context, this paper presents a new STLF scheme based on Adaptative Networks Fuzzy Inference Systems (ANFIS). This ANFIS has an exponential output membership functions (e-ANFIS) and has been trained by means of a novel evolutionary training algorithm (ETA). Due to the computational burden required by ETA, parallel computing was used to eliminate this problem especially for embedded applications. This new scheme has been tested with real data from an automotive factory and it shows better results in comparison with typical adaptative network structures (neural network and ANFIS).
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页数：8
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