SmartCap: Flattening peak electricity demand in smart homes

Barker, Sean; Mishra, Aditya; Irwin, David; Shenoy, Prashant; Albrecht, Jeannie

doi:10.1109/percom.2012.6199851

Cited by 126 publications

(87 citation statements)

References 10 publications

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“…Although this technique has shown a remarkable impact in terms of cost saving, the major drawback associated with this scheme are frequent disconnections and discouragement of consumers in participating in DR programs. In addition, users may feel discomfort while doing their daily life activities, because most of the DR programs are designed for grid stability and peak reduction [5][6][7][8][9].…”

Section: Related Workmentioning

confidence: 99%

“…Although these schemes prove to be efficient in terms of energy management, without the DR program, it is difficult to reduce end user cost. In [9], the authors schedule background loads, like refrigerator and humidifiers, by using the early deadline first technique. Non-background loads are not involved in the scheduling process, because these may affect user comfort.…”

Section: Related Workmentioning

confidence: 99%

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Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

et al. 2016

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Abstract:In this paper, we present an energy optimization technique to schedule three types of household appliances (user dependent, interactive schedulable and unschedulable) in response to the dynamic behaviours of customers, electricity prices and weather conditions. Our optimization technique schedules household appliances in real time to optimally control their energy consumption, such that the electricity bills of end users are reduced while not compromising on user comfort. More specifically, we use the binary multiple knapsack problem formulation technique to design an objective function, which is solved via the constraint optimization technique. Simulation results show that average aggregated energy savings with and without considering the human presence control system are 11.77% and 5.91%, respectively.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

et al. 2016

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“…Thanks to the progress of ubiquitous computing technology, there is a strong anticipation to realize smart daily life support systems such as an energy saving home appliance control system [1], [2], a care-taking system for senior citizen, and a concierge system [3], [4] in a smart home. In particular, the concierge service attracts attention.…”

Section: Introductionmentioning

confidence: 99%

Implementation and Evaluation of Analog-PIR-Sensor-Based Activity Recognition

Fujiwara

Kashimoto

Fujimoto

et al. 2017

SICE Journal of Control, Measurement, and System Integration

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: These days, smart home applications such as concierge service for residents, home appliance control, and so on are attracting attention. To realize these applications, we need a system which recognizes various human activities accurately with a low cost device. There are many studies on the activity recognition in a smart home. We also have proposed an activity recognition technique in a smart home by utilizing digital-output-PIR (passive infrared) sensors, door sensors, and power meters. However, the study has an unsolved issue: we cannot distinguish similar activities happening at the same place, for example, "eating" and "reading" while sitting on a sofa. In order to cope with this challenge, we introduce ALPAS: analog-output-PIR-sensor-based activity recognition technique which recognizes the different activities in the same place. Our technique recognizes user's activity by utilizing machine learning with frequency components of the sensor's output as features. However, because the number of features used in ALPAS is 1000 for each analog PIR-sensor, a large capacity memory is required. To reduce the number of features, we select a part of the sensing data. We call the starting point of the selected data as starting frequency (SF) and ending part as ending frequency (EF). We searched SF and EF using a grid search, and evaluated the recognition accuracy. We evaluated the proposed technique in a smarthome testbed. In the evaluation, five participants performed four different activities while sitting on a sofa. As a result, we achieved F-Measure: 63.9% when the EF is 1.4 Hz, and F-Measure: 50% or lower when the SF is 9.9 Hz or higher.

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“…However, as long as the scheduler ensures the load is on for this time each cycle, it is free to determine when the load is on during the cycle without violating its objective. This freedom is often referred to as slack [2,18].…”

Section: Introductionmentioning

confidence: 99%

“…Prior work focuses on scheduling these shiftable on-off loads either to i) align as much load as possible with renewables [18], ii) defer loads during times of grid constraint [7], or iii) flatten a building's demand profile [2,9]. However, prior work does not address the lack of monetary incentive for consumers to shift loads.…”

Section: Introductionmentioning

confidence: 99%

Incentivizing Advanced Load Scheduling in Smart Homes

Irwin

Shenoy

2013

Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings

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In recent years, researchers have proposed numerous advanced load scheduling algorithms for smart homes with the goal of reducing the grid's peak power usage. In parallel, utilities have introduced variable rate pricing plans to incentivize residential consumers to shift more of their power usage to low-price, off-peak periods, also with the goal of reducing the grid's peak power usage. In this paper, we argue that variable rate pricing plans do not incentivize consumers to adopt advanced load scheduling algorithms. While beneficial to the grid, these algorithms do not significantly lower a consumer's electric bill. To address the problem, we propose flat-power pricing, which directly incentivizes consumers to flatten their own demand profile, rather than shift as much of their power usage as possible to low-cost, off-peak periods. Since most loads have only limited scheduling freedom, load scheduling algorithms often cannot shift much, if any, power usage to low-cost, off-peak periods, which are often many hours in the future. In contrast, flat-power pricing encourages consumers to shift power usage even over short time intervals to flatten demand. We evaluate the benefits of advanced load scheduling algorithms using flat-power pricing, showing that consumers save up to 40% on their electric bill, compared with 11% using an existing time-of-use rate plan.

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SmartCap: Flattening peak electricity demand in smart homes

Cited by 126 publications

References 10 publications

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

Energy Optimization in Smart Homes Using Customer Preference and Dynamic Pricing

Implementation and Evaluation of Analog-PIR-Sensor-Based Activity Recognition

Incentivizing Advanced Load Scheduling in Smart Homes

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