Knowledge Tree
peak-to-average ratio
Also known as: PAR
Facts (24)
Sources
A comprehensive overview on demand side energy management ... link.springer.com Mar 13, 2023 21 facts
referenceThe review article compares various algorithms used in demand-side management (DSM) optimization problems based on factors including energy cost reduction, Peak-to-Average Ratio (PAR), waiting time, power scheduling, voltage limitations, demand response (DR), risk management, client privacy, and carbon emissions.
claimMahmood et al. (2016) recommended a Home Energy Management Controller (HEMC) model to control appliance scheduling, which lowers Peak-to-Average Ratio (PAR) and electricity costs but may lead to energy waste and disregard for environmental concerns.
measurementAhmad et al. (2017) reported that the Genetic Algorithm (GA), Binary Particle Swarm Optimization (BPSO), Bacterial Foraging Optimization (BFO), and Wind-Driven Optimization (WDO) algorithms achieved Peak-to-Average Ratio (PAR) reductions of 14.09%, 3.30%, 22.10%, and 33.54% respectively.
claimRahim et al. (2016b) proposed an energy management effort using Binary Particle Swarm Optimization (BPSO), Ant Colony Optimization (ACO), and Genetic Algorithm (GA) to lower power prices and the peak-to-average ratio (PAR) while incorporating renewable energy sources and storage systems.
claimMahmood et al. (2016) recommended a Home Energy Management Controller (HEMC) model to control appliance scheduling, which lowers user comfort, Peak-to-Average Ratio (PAR), and electricity costs, though it may result in energy waste and disregard environmental concerns.
measurementThe HGPSO algorithm reduced the Peak-to-Average Ratio (PAR) by 25.12% and electric costs by 24.88% according to Ahmad et al. (2017).
referenceThe research article provides a comprehensive comparison of various algorithms used in Demand Side Management (DSM) optimization problems, evaluating them based on energy cost reduction, Peak-to-Average Ratio (PAR), waiting time, power scheduling, voltage limitations, Demand Response (DR), risk management, client privacy, and carbon emissions.
claimRahim et al. (2016) employed ant colony optimization (ACO) based on time-of-use (TOU) and inclining block rates (IBR) to decrease residential energy usage, successfully lowering peak load, peak-to-average ratio (PAR), and energy expenditures without negatively impacting customer satisfaction.
measurementIn a study by Ahmad et al. (2017), the percentage of peak-to-average ratio (PAR) reduction for GA, BPSO, BFO, and WDO algorithms was 14.09%, 3.30%, 22.10%, and 33.54% respectively.
measurementJavaid et al. (2017a) found that the GAPSO algorithm outperformed GA and BPSO in cost and discomfort metrics, reducing peak power use by 27.7794% and peak-to-average ratio (PAR) by 36.39%, while reducing energy consumption costs by 25.2923%.
measurementThe hybrid genetic wind-driven (HGWD) algorithm reduced user comfort by 40%, Peak-to-Average Ratio (PAR) by 17%, and electricity costs by 30%.
claimAmbreen et al. (2017) published a heuristic technique for smart grid management that optimizes home appliance scheduling to reduce costs and peak-to-average ratio (PAR) while maintaining user comfort.
referenceShuja et al. (2019) proposed an efficient scheduling method for smart home appliances using a cost and Peak-to-Average Ratio (PAR) optimization algorithm, published in IEEE Access, 7:102517 (Note: Source text lists journal as IEEE Access, though volume/page data is implied).
claimRahim et al. (2016) employed Ant Colony Optimization (ACO) based on Time-of-Use (TOU) and Inclining Block Rates (IBR) to decrease residential energy usage, successfully lowering peak load, peak-to-average ratio (PAR), and energy expenditures without affecting customer satisfaction.
measurementUsing Genetic Algorithm (GA) scheduling for home appliances reduces energy costs by 52% and peak-to-average ratio (PAR) by 23%, according to Ambreen et al. (2017).
claimNoor et al. (2018) proposed a Game Theory Algorithm (GTA) technique for demand-side management that incorporates storage components to reduce the peak-to-average ratio and smooth demand profile dips caused by supply restrictions.
claimAmbreen et al. (2017) developed a heuristic technique for smart grids that optimizes home appliance scheduling to reduce costs, peak-to-average ratio (PAR), and load, while maintaining user comfort.
measurementThe hybrid genetic wind-driven (HGWD) algorithm reduced user comfort by 40%, Peak-to-Average Ratio (PAR) by 17%, and electricity costs by 30% according to Javaid et al. (2017b).
claimIn the context of energy management optimization, PAR stands for Peak to average ratio.
measurementUsing genetic algorithm (GA) scheduling for home appliances results in a 52% reduction in costs and a 23% reduction in peak-to-average ratio (PAR), according to Ambreen et al. (2017).
claimNoor et al. (2018) proposed a game theory algorithm (GTA) technique for demand-side management that incorporates storage components to reduce the peak-to-average ratio and smooth out demand profile dips caused by supply restrictions, with customer rewards determined by the lowest cost.