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Power Plant Data Filtering Based on Gaussian Naive Bayesian Classification and Prediction Error Method
- Source :
- 2019 Chinese Automation Congress (CAC).
- Publication Year :
- 2019
- Publisher :
- IEEE, 2019.
-
Abstract
- The sufficient utilization of the mega data acquired from operative power units is a promising way leading to high efficient, clean and safe operation of power plant. Hereinto, data filtering is a critical link to the data-driven dynamic model identification aiming at optimizing the process control. The machine learning is an advantageous approach for filtering usable data from mega databases for identification due to its effective statistical learning strategy to the field data with noises, disturbances and coupling quantities. Therefore, a data filtering method of combining Gaussian Naive Bayesian classifier and prediction error method (Gaussian NB-PEM) is proposed in this paper. Firstly, variables associated with identification model are selected by analyzing the characteristics of the process. Secondly, the GaussianNB classifier is used for coarse data filtering by calculating the priori probability of each attribute from training sample set and the probability with all possible values of the known categories for testing sample set. Thirdly, the prediction error method is used for further data filtering based on model fitting. By using the filtered closed-loop data, the dynamic characteristics of the superheated steam temperature is modeled and verified by closed-loop control simulation, showing the validity of the Gaussian NB-PEM data filtering method.
- Subjects :
- Imagination
A priori probability
Computer science
business.industry
Gaussian
Mean squared prediction error
media_common.quotation_subject
010401 analytical chemistry
System identification
Pattern recognition
02 engineering and technology
021001 nanoscience & nanotechnology
01 natural sciences
0104 chemical sciences
symbols.namesake
Naive Bayes classifier
symbols
Artificial intelligence
0210 nano-technology
business
Classifier (UML)
media_common
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- 2019 Chinese Automation Congress (CAC)
- Accession number :
- edsair.doi...........a3f7d9baa222a831cc4e408b897263f5