Michéle Weisbach, Kay Herklotz, H. Fechtner, U. Spaeth, Bela Gipp, B. Schmuelling
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Predicting Power Demand in Urban Transportation Systems using an Evolutionary Neural Network
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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