Integrated Bio-Inspired Approach for Industrial Energy Management Systems

Problem of energy management in industrial sector has been well studied. There exist numerous techniques to handle the problem of energy management in industrial sector. The existing methods perform the selection of power source according to the generic energy consumption and capacity of source. However, the methods do not produce higher performance in energy management and lacks to support smooth functioning of industrial units. To stimulate such energy management performance, an Integrated Bio-Inspired Industrial Energy Management System (IBIEMS) is presented in this article. The proposed model uses genetic algorithm for the identification of energy sources to support industrial units. Further, the model generates number of population which has been optimized with the Particle swarm optimization technique. The GA generates population where the PSO performs selection of resource for the duty cycle considered. To perform this, the model identifies the set of energy resources available in the grid environment. With the sources identified, the set of resources which are available at any point of time is selected to perform cross over and mutation with GA. For each mutation, the method applies PSO towards measuring the fitness value for the population produced. In this way, the method computes Energy Suction Rate (ESR) for various industrial units and estimates Power Fitness Value (PFV). The PSO algorithm computes PFV towards selection of energy resource for any cycle and triggers the sources accordingly. The energy management system designed monitor the ESR value for various units and controls the functioning of different power sources. The proposed model improves the performance of energy efficiency and energy management.

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