Industries are consuming more than 27% of the total generated energy in the world, out of which 50% is used by different machines for processing, producing, and assembling various goods. Energy shortage is a major issue of this biosphere. To overcome energy scarcity, a challenging task is to have optimal use of existing energy resources. An efficient and effective mechanism is essential to optimally schedule the load units to achieve three objectives: minimization of the consumed energy cost, peak-to-average power ratio, and consumer waiting time due to scheduling of the load. To achieve the aforementioned objectives, two bio-inspired heuristic techniques—Grasshopper-Optimization Algorithm and Cuckoo Search Optimization Algorithm—are analyzed and simulated for efficient energy use in an industry. We considered a woolen mill as a case study, and applied our algorithms on its different load units according to their routine functionality. Then we scheduled these load units by proposing an efficient energy management system (EMS). We assumed automatic operating machines and day-ahead pricing schemes in our EMS.
Efficient Energy Management System Based on IoT
