Predicting Power Demand in Urban Transportation Systems using an Evolutionary Neural Network

Michéle Weisbach, Kay Herklotz, H. Fechtner, U. Spaeth, Bela Gipp, B. Schmuelling
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Abstract

This paper presents concisely one of the main topics of a research project, concerning the sustainable linking between smart traffic systems and smart grids by an efficient energy management – deployed in Germany. Therefore, an evolutionary neural etwork modification algorithm is developed to predict the power demand of Battery Overhead Line Buses (BOB), which were regarded as moving energy storages. This knowledge allows a flexible usage of these battery capacities e.g. to harmonize the general catenary grid load.
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基于进化神经网络的城市交通系统电力需求预测
本文简要介绍了一个研究项目的主要主题之一,该项目涉及通过高效能源管理在德国部署的智能交通系统和智能电网之间的可持续连接。为此,提出了一种进化神经网络修正算法来预测作为移动储能系统的蓄电池架空母线(BOB)的电力需求。这些知识允许灵活地使用这些电池容量,例如,协调一般接触网电网负载。
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