TY  - CONF
A1  - Graus, Marcel
A1  - Niemietz, Philipp
A1  - Rahman, Mohammad Touhidur
A1  - Hiller, Michaela
A1  - Pahlenkemper, Markus
A2  - Radil, Lukáš
A2  - Macháček, Jan
A2  - Morávek, Jan
A2  - Ptáček, Michal
T1  - Machine learning approach to integrate waste management companies in micro grids
T2  - 2018 19th International Scientific Conference on Electric Power Engineering (EPE)
N2  - The integration of renewable energies in a local industrial environment is an urgent task to reduce greenhouse gas emissions. Their energy intensive processes and local energy generation make waste management companies to optimal areas to analyze micro grids. The combination of the main task to process arriving waste and the reaction on micro grid needs without disregarding user preferences is the challenge that is focused with the following approach applying machine learning techniques.
First, the amount of waste is predicted with an artificial neural network. Then, the waste processing is optimized via an augmented Lagrangian algorithm regarding the energy costs that are based on volatile energy prices influenced from renewable energies. In addition, the optimization regards user preferences, which are learned from a user feedback with a support vector machine.
For the user interaction, an active learning paradigm is used. The approach is applied on biological waste treatment process in the waste management company of the district of Warendorf. The results show that the energy consumptions can be controlled in a micro grid context within the frame of user preference.
KW  - machine learning
KW  - micro grids
KW  - waste management companies
Y1  - 2023
UR  - https://epub.fir.de/frontdoor/index/index/docId/2616
SN  - 978-1-5386-4612-0
SP  - 103
EP  - 108
PB  - IEEE
CY  - Piscataway (NJ)
ER  -