In this work, we propose a new street lighting energy management system in order to reduce energy consumption. The key idea we want to accomplish is that of "energy on demand" meaning that energy, in this case light, is provided only when needed. In order to achieve this goal, it is critical to have a reliable demand model, which in the case of street lighting turns out to be a traffic flow rate forecasting model. In order to achieve this goal, several methods on the 1-h prediction have been compared and the one providing the best results is based on artificial neural networks. Moreover, several control strategies have been tested and the one which gave the best energy savings is the adaptive one we carried out. Experimentation has been carried out on real data and the study shows that with the proposed approach, it is possible to save up to 50 % of energy compared to no regulation systems. © 2013 Springer Science+Business Media Dordrecht.
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