Multi-agent Support for Street Lighting Modernization Planning

Abstract

The inherent problem related to the maintenance of public lighting infrastructures is replacing (retrofitting) aging fixtures with the new ones. Sometimes municipalities decide to retrofit installations which did not reach their lifespan limit. The example is replacing the high-intensity discharge lamps with much more energy-efficient LEDs (Light Emitting Diodes) or induction bulbs. Any retrofit approach, however, generates costs which are to be reduced. Thus, the retrofit strategy is aimed at finding a trade-off between financial outlays and potential benefits expressed in terms of power savings, payback period etc. Financial constraints imposed on an investment usually delimit a retrofit scope to restricted parts of entire public lighting installation. A key problem which arises then is: which luminaires should be selected for retrofitting, to make it the most profitable? In this work we propose using an agent-based approach to response this question by preparing a high-level strategy for optimal retrofits of large-scale lighting systems (e.g., covering entire city area).