Effective energy consumption scheduling in smart homes

Longe, Omowunmi Mary; Ouahada, Khmaies; Rimer, Suvendi; Zhu, Hailing; Ferreira, H.C.

doi:10.1109/afrcon.2015.7331917

Cited by 13 publications

(7 citation statements)

References 6 publications

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“…This is because the consumers under Scenario 3 had purchased energy from the grid at non-peak times (low energy price periods) and stored it in their batteries. This stored energy is primarily discharged locally to household appliances during morning and evening peak periods, and thereby mitigates the peak period demand dissatisfaction that characterizes the Scenario 2 scheduling algorithm like other DSM energy scheduling algorithms in the literature [4,5,7]. The effect of DSM scheduling in Scenarios 2 and 3 on dissatisfaction cost is shown in Figure 5.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

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Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling

et al. 2017

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Abstract:The role of Demand Side Management (DSM) with Distributed Energy Storage (DES) has been gaining attention in recent studies due to the impact of the latter on energy management in the smart grid. In this work, an Energy Scheduling and Distributed Storage (ESDS) algorithm is proposed to be installed into the smart meters of Time-of-Use (TOU) pricing consumers possessing in-home energy storage devices. Source of energy supply to the smart home appliances was optimized between the utility grid and the DES device depending on energy tariff and consumer demand satisfaction information. This is to minimize consumer energy expenditure and maximize demand satisfaction simultaneously. The ESDS algorithm was found to offer consumer-friendly and utility-friendly enhancements to the DSM program such as energy, financial, and investment savings, reduced/eliminated consumer dissatisfaction even at peak periods, Peak-to-Average-Ratio (PAR) demand reduction, grid energy sustainability, socio-economic benefits, and other associated benefits such as environmental-friendliness.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Recent DSM studies [2][3][4][5][6][7][8], have presented some techniques for appliance energy consumption scheduling, peak demand reduction (PDR), and Peak-to-Average-Ratio (PAR) demand reduction with some level of consumer preferences.…”

Section: Introductionmentioning

confidence: 99%

Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling

et al. 2017

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“…An artificial fish swarm algorithm and genetic algorithm have been used to reduce the peak to average ratio (PAR) and cost of electricity by [78]. An electricity optimization rescheduling scheme using mixed integer linear programming method (MELP) and a daily maximum energy scheduling device for South Africa has been proposed by [79]. Through the device, consumers can efficiently reduce electricity consumption.…”

Section: Algorithms and Techniques For Energy Optimization Through Scmentioning

confidence: 99%

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

et al. 2019

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In recent years, due to the unnecessary wastage of electrical energy in residential buildings, the requirement of energy optimization and user comfort has gained vital importance. In the literature, various techniques have been proposed addressing the energy optimization problem. The goal of each technique is to maintain a balance between user comfort and energy requirements, such that the user can achieve the desired comfort level with the minimum amount of energy consumption. Researchers have addressed the issue with the help of different optimization algorithms and variations in the parameters to reduce energy consumption. To the best of our knowledge, this problem is not solved yet due to its challenging nature. The gaps in the literature are due to advancements in technology, the drawbacks of optimization algorithms, and the introduction of new optimization algorithms. Further, many newly proposed optimization algorithms have produced better accuracy on the benchmark instances but have not been applied yet for the optimization of energy consumption in smart homes. In this paper, we have carried out a detailed literature review of the techniques used for the optimization of energy consumption and scheduling in smart homes. Detailed discussion has been carried out on different factors contributing towards thermal comfort, visual comfort, and air quality comfort. We have also reviewed the fog and edge computing techniques used in smart homes.

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“…The DMES device was first proposed by the authors for FRP customers in [21]. However for TOU consumers, (7) would not hold since is not same within 24 hours, but depends on the time of use of energy in the day and season.…”

Section: Optimized Household Energy Expenditure Formulationmentioning

confidence: 99%

Optimization of energy expenditure in smart homes under time-of-use pricing

Longe

Ouahada

Rimer

et al. 2015

2015 IEEE Innovative Smart Grid Technologies - Asia (ISGT ASIA)

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Growing peak demand has necessitated the introduction of Time-of-Use (TOU) pricing to Demand Side Management (DSM) in order to cause some peak demand to be shifted from peak to off-peak periods. Therefore, in this work, a Daily Maximum Energy Scheduling (DMES) -DSM technique is proposed. The DMES-DSM device is proposed to be installed into consumers' smart meters and schedule energy consumption for smart appliances. The DMES-DSM technique was verified with real household data and shown to be capable of optimizing households' monthly energy expenditure below approved national energy expenditure threshold and also offer Peak Demand Reduction (PDR). It offered the household considered an average monthly financial savings of 22.44% and 36.73% in summer and winter respectively on electricity bills. Utility can also benefit from the PDR for grid stability and sustainability. Also, the optimized consumption pattern differs only slightly from initial consumption pattern for enhanced consumer satisfaction. Index Terms--DailyMaximum Energy Scheduling (DMES), Demand Side Management (DSM), Household income, Smart appliances, Time-of-Use (TOU).

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Effective energy consumption scheduling in smart homes

Cited by 13 publications

References 6 publications

Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling

Distributed Demand Side Management with Battery Storage for Smart Home Energy Scheduling

A Review on Energy Consumption Optimization Techniques in IoT Based Smart Building Environments

Optimization of energy expenditure in smart homes under time-of-use pricing

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