Power Demand Prediction of Battery Overhead Line Buses based on a Neural Network Optimization

Autor:	Utz Spaeth, Milad Ghobadi, Michele Weisbach, Benedikt Schmuelling
Rok vydání:	2020
Předmět:	Battery (electricity) Smart grid Artificial neural network Energy management Computer science Catenary Energy consumption Grid Overhead line Reliability engineering
Zdroj:	2020 IEEE Green Technologies Conference(GreenTech).
DOI:	10.1109/greentech46478.2020.9289715
Popis:	This short paper presents concisely one of the main topics of a research project, concerning the sustainable linking between traffic and energy management - deployed at Solingen (North Rhine-Westphalia - Germany). Therefore, Battery Overhead Line Buses (BOB) were regarded as moving energy storages which allows a flexible usage of these capacities to harmonize the general grid load of the catenary. The power demand prediction, which is necessary for a daily energy consumption forecast and related regulation algorithms, is being realized by a neural network.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::a8e6c23f13b2d0cbe0f946b0923df6c9 https://doi.org/10.1109/greentech46478.2020.9289715 Zobrazit plný text záznamu