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Cited by 568

The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.

Appliance Commitment for Household Load Scheduling

Du, Pengwei, Lu, Ning·2011·IEEE Transactions on Smart Grid

Abstract

This paper presents a novel appliance commitment algorithm that schedules thermostatically controlled household loads based on price and consumption forecasts considering users' comfort settings to meet an optimization objective such as minimum payment or maximum comfort. The formulation of an appliance commitment problem is described using an electrical water heater load as an example. The thermal dynamics of heating and coasting of the water heater load is modeled by physical models; random hot water consumption is modeled with statistical methods. The models are used to predict the appliance operation over the scheduling time horizon. User comfort is transformed to a set of linear constraints. Then, a novel linear-sequential-optimization-enhanced, multiloop algorithm is used to solve the appliance commitment problem. The simulation results demonstrate that the algorithm is fast, robust, and flexible. The algorithm can be used in home/building energy-management systems to help household owners or building managers to automatically create optimal load operation schedules based on different cost and comfort settings and compare cost/benefits among schedules.

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Metadata

Authors
Du, P., & Lu, N.
Venue
IEEE Transactions on Smart Grid
Published
2011-06
Volume
2
Issue
2
Pages
411-419
ISSN
  • 1949-3053 (ISSN-L)
  • 1949-3061
OpenAlex
W2122723365
Cited by
568
References
1
FWCI
28.30
Type
journal article
Language
en
Open access
closed

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CoChat. (2026). Citation check: Appliance Commitment for Household Load Scheduling. https://app.cochat.ai/papers/10.1109/tsg.2011.2140344
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Du, P., & Lu, N. (2011). Appliance Commitment for Household Load Scheduling. IEEE Transactions on Smart Grid. https://doi.org/10.1109/tsg.2011.2140344

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.