Case-Based Reasoning Combined with Information Entropy and Principal Component Analysis for Short-Term Load Forecasting

2007 ◽  
Author(s):  
Jinsha Yuan ◽  
Li Qu ◽  
Weihua Zhang ◽  
Li Li
Keyword(s):  
Principal Component Analysis ◽  
Information Entropy ◽  
Load Forecasting ◽  
Principal Component ◽  
Component Analysis ◽  
Case Based Reasoning ◽  
Short Term ◽  
Short Term Load Forecasting ◽  
Case Based

Multivariate Principal Component Analysis and Case-Based Reasoning for monitoring, fault detection and diagnosis in a WWTP

2011 ◽  
Vol 64 (8) ◽  
pp. 1661-1667 ◽  
Author(s):  
Magda Ruiz ◽  
Gürkan Sin ◽  
Xavier Berjaga ◽  
Jesús Colprim ◽  
Sebastià Puig ◽  
...  
Keyword(s):  
Principal Component Analysis ◽  
Fault Detection ◽  
Treatment Plant ◽  
Principal Component ◽  
Component Analysis ◽  
Fault Detection And Diagnosis ◽  
Case Based Reasoning ◽  
Process Data ◽  
Predictive Diagnosis ◽  
Case Based

The main idea of this paper is to develop a methodology for process monitoring, fault detection and predictive diagnosis of a WasteWater Treatment Plant (WWTP). To achieve this goal, a combination of Multiway Principal Component Analysis (MPCA) and Case-Based Reasoning (CBR) is proposed. First, MPCA is used to reduce the multi-dimensional nature of online process data, which summarises most of the variance of the process data in a few (new) variables. Next, the outputs of MPCA (t-scores, Q-statistic) are provided as inputs (descriptors) to the CBR method, which is employed to identify problems and propose appropriate solutions (hence diagnosis) based on previously stored cases. The methodology is evaluated on a pilot-scale SBR performing nitrogen, phosphorus and COD removal and to help to diagnose abnormal situations in the process operation. Finally, it is believed that the methodology is a promising tool for automatic diagnosis and real-time warning, which can be used for daily management of plant operation.

Sign in / Sign up

Export Citation Format

Share Document