1 / 14

Enhanced Equal Frequency Partition Method for the Identification of a Water Demand System

Enhanced Equal Frequency Partition Method for the Identification of a Water Demand System. T. Escobet R.M. Huber A. Nebot F.E. Cellier Dept ESAII IRI Dept. LSI ECE Dept. UPC UPC/CSIC UPC UofA. Introduction.

booth
Download Presentation

Enhanced Equal Frequency Partition Method for the Identification of a Water Demand System

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript


  1. Enhanced Equal Frequency Partition Method for the Identification of a Water Demand System T. Escobet R.M. Huber A. Nebot F.E. Cellier Dept ESAII IRI Dept. LSI ECE Dept. UPC UPC/CSIC UPC UofA

  2. Introduction • The Equal Frequency Partition is one of the simplest unsupervised partitioning methods. • However, EFP is sensitive to data distribution. • A good partitioning is obtained if all possible behaviors of the system are represented with a comparable number of occurrences.

  3. Introduction • The first goal is to present an enhancement to the EFP method to be used within the FIR methodology that allows to reduce, to some extent, the data distribution dependency. • The second goal is to use the EEFP method within the discretization step of FIR for the identification of a model of a water demand system.

  4. Enhanced EFP method • The EEFP method eliminates multiple observations of the same behavioral pattern. δ = range of similar observations. α = minimum number of occurrences to assume that this behavioral pattern is over-represented.

  5. FIR fuzzification process • Then applies EFP to the remaining set of significantly different patterns to decide on a meaningful set of landmarks.

  6. Water demand application • The system to be modeled is the water distribution network of the city of Sintra in Portugal.

  7. Water demand application • The water demands for each reservoir are measured data stemming from the water network. • The other input variables are obtained from the simulation of a control modelof the water demand system.

  8. Discretization of system variables • Demand 1 (Mabrao reservoir) α=10% δ=1%

  9. Discretization of system variables • Second valve α=10% δ=1%

  10. Discretization of system variables • The last input variable is the state of the pumps. • Each pump is composed of two motors, that can either be both stopped, both pumping, or one stopped and one pumping.

  11. Discretization of system variables • Pressure-flow at node 4 α=10% δ=1%

  12. Pressure-flow models errors

  13. Prediction of the pressure-flow at node 4 FIR prediction with EFP (upper) and EEFP (lower)

  14. Conclusions • In this paper an enhancement to the classical Equal Frequency Partition method is presented. • The EEFP method allows to obtain a better distribution of the data into classes. • A real application i.e. water distribution networkis studied. • The prediction errors obtained when the EEFP method is used in the fuzzification process are lower than the ones obtained when the classical EFP method is used.

More Related