Case Study on using K-Means Clustering Technique for Energy saving in
Pressurized Irrigation Networks
Abstract
With the continuously increasing cost of energy, conservation of energy
in pressurized irrigation networks has become an important goal. Such
networks are usually designed to irrigate sectors of approximately equal
areas in turns. Pumps are often operated to guarantee the absolute
maximum head required for all sectors. This design criterion, however,
does not guarantee minimum energy consumption. In this study, the
k-means clustering technique is used for grouping (sectoring) hydrants
with the same characteristics to minimize energy consumption. Various
dimensionless parameters are used to identify hydrants characteristics,
the relative elevation z*, the relative distance l*, and the relative
head h*. These parameters were combined with different integrations to
determine the best combinations from an energy point of view. MATLAB -
EPANET Toolkit is used to implement the suggested clustering technique
and test the impact of proposed management on energy consumption. The
proposed methodology is applied to a drip irrigation network at Kostol
area, Egypt. Results show that sectoring the study area using the
k-means technique based on dimensionless parameters leads to energy
savings up to 16.23% for the whole irrigation season.