User-sensitive scheduling of home appliances

Bapat, Tanuja; Sengupta, Neha; Ghai, Sunil Kumar; Arya, Vijay; Shrinivasan, Yedendra; Seetharam, Deva P.

doi:10.1145/2018536.2018546

Cited by 51 publications

(43 citation statements)

References 8 publications

(10 reference statements)

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“…In [11], the authors consider optimal household appliance scheduling for dynamic pricing. They propose a system, Yupik that determines preferred time of use for individual appliances and generates appliance usage schedules to minimize both a household's energy costs and potential lifestyle disruptions.…”

Section: Related Workmentioning

confidence: 99%

iDR

Chandan

Ganu

Wijaya

et al. 2014

Proceedings of the 5th International Conference on Future Energy Systems

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Peak demand is a major challenge for power utilities across the world. Demand Response (DR) is considered to be effective in addressing peak demand by altering consumption of end consumers, so as to match supply capability. However, an efficient DR system needs to respect end consumer convenience and understand their propensity of participating in a particular DR event, while altering the consumer demand. Understanding such preferences is non-trivial due to the large-scale and variability of consumers and the infrastructure changes required for collecting essential (smart meter and/or appliance specific) data.In this paper, we propose an inclusive DR system, iDR, that helps an electricity provider to design an effective demand response event by analyzing its consumers' houselevel consumption (smart meter) data and external context (weather conditions, seasonality etc.) data. iDR combines analytics and optimization to determine optimal power consumption schedules that satisfy an electricity provider's DR objectives -such as reduction in peak load -while minimizing the inconvenience caused to consumers associated with alteration in their consumption patterns. iDR uses a novel context-specific approach for determining end consumer baseline consumptions and user convenience models. Using these consumer specific models and past DR experience, iDR optimization engine identifies -(i) when to execute a DR event, (ii) who are the consumers to be targeted for the DR, and (iii) what signals to be sent. Some of iDR's capabilities are demonstrated using real-world house-level as well as appliance-level data.

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Section: Related Workmentioning

confidence: 99%

iDR

Chandan

Ganu

Wijaya

et al. 2014

Proceedings of the 5th International Conference on Future Energy Systems

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“…is carried out through a "cost-to-arrive" function, which turns out to be J (8). In addition, for k ≥ 1, w := S v (k), it holds that…”

Section: Dynamic Programming With Tree Interaction Graphmentioning

confidence: 99%

Nonserial dynamic programming with applications in smart home appliances scheduling - Part II: Nonserial dynamic programming

Sou

Johansson

2014

2014 European Control Conference (ECC)

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In this paper a dynamic programming (DP) solution approach to a nonconvex resource allocation problem (RAP) is presented. The problem in this paper generalizes the smart home appliances scheduling problem introduced in the companion paper (i.e., Part I). The computation difficulty with solving the RAP depends on the decision variable coupling, which can be described by an interaction graph. This paper proposes a DP algorithm to solve the RAP in the special setting where the interaction graph is a tree. This extends the applicability result of DP to RAP beyond the standard serial case where the interaction graph is a line. The extension of the result is achieved by establishing that even in the tree case DP computation effort is polynomial in the number of decision variables. For RAP in its general form, this paper proposes a modification to the nonserial DP procedure originally introduced by Bertele and Brioschi. Contrary to that of the previous method, the computation effort of the proposed method can be characterized by a well-known minimum feedback vertex set problem. Case studies on known examples indicate that both nonserial DP methods are similarly efficient.

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“…These constraints are used to generate a revised schedule. The specific algorithm used to create a revised schedule is left unspecified in DRSim as many candidates can be used [20]. Finally, we provide an extensible utility loss function to compute user's inconvenience due to the shift operations.…”

Section: Modeling Dr Signal Response and Actionsmentioning

confidence: 99%

“…Along with this, we augment a survey dataset by interviewing the same users to obtain their static profiles: gender, age, occupation, and income group, number of rooms and type (kitchen, bed, dining etc), electrical appliances in each room, and their appliance usage pattern, i.e., the set of appliances they use while performing a particular activity. A second data set provides us with appliance consumption logs of from [20], to obtain different operational states of appliances and their corresponding electricity consumptions.…”

Section: Implementation and Experimentsmentioning

confidence: 99%

DRSim: A cyber physical simulator for Demand Response systems

Wijaya

Banerjee

Ganu

et al. 2013

2013 IEEE International Conference on Smart Grid Communications (SmartGridComm)

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Abstract-Demand Response (DR) is a mechanism in which electricity consumers alter their demand in response to power grid's supply and economic conditions. DR programs have the potential to improve resource efficiency, sustainability, grid reliability and economic viability by providing tighter alignment between demand and supply. However, implementing DR program is a non-trivial task as it requires good knowledge of electricity consumption preferences, economic models and contextual factors. Developing such knowledge through real world studies can be expensive and be time consuming. As a result, utility companies have been finding it complicated to analyze potential viability and return on investments of DR programs for various 'what-if' scenarios.To address this problem, we present DRSim -a cyberphysical simulator that allows utility companies to study demand side participation aspects of communities with various practical scenarios. DRSim is based on the principles of agent-oriented modeling of users' behavior and context. It is able to model the emergent behavior of a community based on real data traces that contain partial information about the environment. DRSim is a highly extensible framework to accommodate new data sources, new analytical functionalities and evolve its modeling power. Feasibility experiments show the modeling and analysis potential of DRSim in practical settings. I. INTRODUCTIONTowards a more sustainable environment, several governments have defined specific goals for reducing total energy consumption (e.g., EU aims to reduce energy consumption by 20% by 2020). With the emergence of technology to support Demand Response (DR), DR programs are known to be effective for reducing energy consumption and peak demand [1], [2]. These DR systems send an "alter demand" signal to electricity customers to alter their consumption at critical times, e.g. during supply-demand imbalance or in response to grid market price fluctuations.While residential power consumption forms a substantial portion of total energy consumption [3], [4], little is known today on -how much energy is consumed by a particular activity, what human tasks lead to peak usage times, how consumption varies across different customer segments, and which DR programs (e.g. price based [5] or incentive based) are effective for the specific communities. Moreover, the residential DR programs have a major challenge of receiving a good DR participation from a large number of small electricity consumers, where there is much less control and more autonomy [6], [7].The success of a residential DR program largely depends on proper understanding regarding the driving factors of the energy demand for the community and deriving useful conclusions on the demand dynamics. There are various factors

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User-sensitive scheduling of home appliances

Cited by 51 publications

References 8 publications

iDR

iDR

Nonserial dynamic programming with applications in smart home appliances scheduling - Part II: Nonserial dynamic programming

DRSim: A cyber physical simulator for Demand Response systems

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