IoT based smart and intelligent smart city energy optimization

With the effective result of IoT architecture in all research areas, we propose IoT framework based energy efficient smart and intelligent street road lighting system that consist of IoT sensor bases smart electric pole with controller for tuning LED lamps. In our work we replace traditional metal halide lamps with mesophic design LED lamps based on human eye sensitivity and it provides significant saving of energy too. Based on the sensor unit information about traffic flow and presence and absence of occupant smart and intelligent decision making module compute intensity level that generate different width pulse using PWM dimming system which triggers LED power switch via DALI controller mounted inside LED Light Controller. For efficient use of sustainable energy resources we use sustainable power systems that consist of PV solar panel units, Battery storage systems and smart electric power grids. For the charging battery system we use MPPT based dynamic battery charging algorithm. From experiment and simulated result, we observe that proposed energy efficient Smart Street lighting saves considerable amount of energy during peak and off peak hours not only at highway roads but at residential and sub-urban pedestrian areas also. It will finally decrease the consumption of energy and carbon emission. We perform comparative result analysis of proposed systems with existing lighting systems based on energy consumption. Our experimentation result also represents the effectiveness of the proposed dynamic battery charging algorithm based on the battery storage PV solar panel system performance.

Automated summary

In this study, we propose an energy efficient smart and intelligent street lighting system based on IoT architecture. The system consists of IoT sensor-based smart electric poles with LED lamps controlled by a controller. By replacing traditional lamps with LED lamps based on human eye sensitivity, significant energy savings are achieved. The system uses a smart and intelligent decision-making module to compute the intensity level based on sensor information about traffic flow and occupancy. This module generates different width pulses using a PWM dimming system, triggering a LED power switch via a DALI controller. Sustainable power systems, including PV solar panels, battery storage systems, and smart electric power grids, are used for efficient use of sustainable energy resources. A dynamic battery charging algorithm based on MPPT is used for the battery charging system. Experimental and simulation results show that the proposed system saves a considerable amount of energy during peak and off-peak hours, not only on highways but also in residential and suburban pedestrian areas. This system can ultimately reduce energy consumption and carbon emissions. Comparative analysis with existing lighting systems based on energy consumption is conducted, and the effectiveness of the proposed dynamic battery charging algorithm is demonstrated based on the performance of the battery storage PV solar panel system.