.png){kind=icon}
Return
Short term power dispatch using neural network based ensemble classifier
.png){kind=icon}
PRE
AI
DOI：10.1016/j.est.2020.102101.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
This work reports the application of ensemble artificial neural networks, a machine learning technique, in the solution of short term optimal power generation on the IEEE 30-bus test system. The study, carried out using MATLAB, is being reported. The motive for using ensemble artificial neural networks has been to take advantage of multiple parallel processors computing rather than the traditional serial computation. The Bootstraps are obtained through small bags in the Bagging algorithm and are combined by averaging. Employing ensemble neural networks reduces bias and variance in machine learning so that the overall error is reduced, and hence better prediction can be achieved. The results obtained from the proposed algorithm are compared with those from the other heuristic and conventional techniques to validate the effectiveness of the proposed methodology.
Keywords:
Bagging
Bootstraps
Optimal power generation
Ensemble artificial neural networks
Neural networks
Small bags
Unit commitment
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
9.8
Papers:
2.3W
Citations:
10.1W
Organization
Cited Papers
ShareSaveShareSave
ShareSaveCurrent Harmonic Suppression Technology for Dual Three-Phase Permanent Magnet Synchronous Motor
PREAI
ShareSaveShareSaveA modified bacteria foraging based optimal power flow framework for Hydro-Thermal-Wind generation system in the presence of STATCOM.png)
ENERGY
ENERGYIF9.4
PREAI
ShareSave
View more