Machine Learning
A methodology for estimating electricity consumption for rice crops that use flood irrigation, in the city of Uruguaiana, Rio Grande do Sul, implementing classification using artificial intelligence techniques (clustering, k-means and random forest) is presented.
Abstract
: The difficulty of detecting non-technical losses by electric energy concessionaires has been a great and constant challenge. Inspecting consumer units located in rural areas demands excessive time and expenses on the part of concessionaires, due to the distance from urban centers and the difficulty of access, without there being a previous technical indication of the occurrence of Non-Technical Losses. This work aims to present a methodology for estimating electricity consumption for rice crops that use flood irrigation, in the city of Uruguaiana, Rio Grande do Sul, implementing classification using artificial intelligence techniques (clustering, k-means and random forest)