Publication Type:

Book Chapter

Source:

Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, Springer, Cham, Volume 28, p.705-721 (2018)

URL:

https://link.springer.com/chapter/10.1007/978-3-319-71767-8_62

Keywords:

Facial recognition system, Image analytics, Internet of things, Predictive analysis, Smart sensor, Threshold, Wi-Fi

Abstract:

Large educational institutes, organization, and industries face large challenges on energy utilities, consumption and its management strategies. But smart energy management technology solutions help the high energy consumption complexities during while putting the best and greenest foot forward. Smart Energy Management technology solutions, improve and respond quickly to power spikes at times of demand, expedite data gathering, reporting and regulatory compliance, automate services to control operating costs and enable to save energy. Connecting smart meters to data stores requires a reliable, intelligent network. The Smart Energy Management System introduced here deals with a device level analysis that gives information of the device that has caused the peak rise in the total power consumption of the organization. Predictive analysis technique is used on the database to predict the future maximum demand and load balancing technique is applied to reduce the consumption of power from generator source. Therefore the total power consumption from exceeding the maximum demand can be avoided and the maximum demand of the power supply for the organization can be maintained. Further, on application of AI techniques this system control becomes fully automated.

Notes:

cited By 0

Cite this Research Publication

S. Birindha, Anantha Narayanan V., and Dr. Bhagavathi Sivakumar P., “Smart Energy Management System Based on Image Analytics and Device Level Analysis”, in Computational Vision and Bio Inspired Computing. Lecture Notes in Computational Vision and Biomechanics, vol. 28, Springer, Cham, 2018, pp. 705-721.