Comparative Analysis of Particle Swarm Optimization and Differential Evolution Algorithm to Curtail Power Consumption using Smart Energy Meter Analytics

Abstract

Consumption of energy has increased multifarious in recent years and its appropriate management is the need of the hour. Energy management system makes use of various tools and technologies to monitor and optimize energy consumption in households and workplaces. Use of several stochastic and deterministic algorithms for the purpose of energy management has rapidly gained substantial importance recently owing to their flexibility, ease of implementation and efficient results. This paper presents two algorithms based on Particle Swarm Optimization (PSO) and Differential Evolution (DE) algorithm for optimizing consumption of power using smart energy meter data analytics. Both are meta-heuristic algorithms that can easily solve multifaceted problems in engineering. The power consumption equation has been optimized using first PSO then DE. The validity and efficiency of the algorithm is tested using the real time data obtained from smart energy meter and a comparative study is presented. The modeling and simulation is carried on MATLAB platform and the results depict that both presented algorithms can ominously reduce the power consumption. With PSO approximately 11.5% reduction in power can be obtained and DE can reduce up to 9.4% power in best-case scenario making PSO superior to DE.