A street lighting control system based on holonic structures and traffic system

• Published in: 2011 3rd International Conference on Computer Research and Development Vol. 1; pp. 92 - 96
• Main Authors: Moghadam, M H, Mozayani, N
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-03-2011
• Subjects: Artificial intelligence; Energy consumption; holonic multi agent system; Lighting; Multiagent systems; Smart control; street lighting; traffic; Traffic control; Turning
• ISBN: 1612848397; 9781612848396
• DOI: 10.1109/ICCRD.2011.5763980

Street lighting system is one of the main sectors of energy consumers in the area of the electrical energy consumption in the country. Since suitable lighting system plays an important role in terms of individual and social security in the city, providing an optimal management plan in line with the aim of saving consumption of electrical energy while providing appropriate lighting levels, can be a great help to the field of energy consumption. In this article we have tried to provide a comprehensive and overall plan for controlling and intelligent managing the street lighting system based on passages traffic using holonic multi agent systems. This approach controls the lighting system by intelligent agents in a holonic organization mounted on the traffic infrastructure. This control is done at different levels in centralized and distributed form. We can see two levels in our holonic multi agent system. In the high level of the system, there are geographical holons and their heads with strategic tasks. In the low level of the structure, there are learning agents with tactical tasks in geographical holons. They are main controllers installed in the streets. They form high consumption and low consumption holons dynamically, according to their decision for controlling the street lighting. The head of geographical holon also supervise the learning agents and formation of the consumption holons. We show that our approach can control the street lighting dynamically and efficiently in different conditions. The results of implementing this approach on a simulated network of streets and their traffic, shows that this method can lead to significant decreases in electrical energy consumption.