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dc.contributor.author
Bragagnolo, Sergio Nicolás
dc.contributor.author
Schierloh, R. M.
dc.contributor.author
Vega, Jorge Ruben
dc.contributor.author
Vaschetti, Jorge Carlos
dc.date.available
2024-01-03T14:05:59Z
dc.date.issued
2022-10
dc.identifier.citation
Bragagnolo, Sergio Nicolás; Schierloh, R. M.; Vega, Jorge Ruben; Vaschetti, Jorge Carlos; Demand response strategy applied to planning the operation of an air conditioning system: Application to a medical center; Elsevier; Journal of Building Engineering; 57; 10-2022; 1-16
dc.identifier.issn
2352-7102
dc.identifier.uri
http://hdl.handle.net/11336/222216
dc.description.abstract
Large air conditioning systems, such as those used in shopping and health centers, typically demand high amounts of energy. Several air conditioning technologies and energy management strategies seek to minimize consumption to reduce billing expenses and improve system efficiency. This work proposes a demand response framework to plan the daily operation of an air conditioning system with the aim of minimizing the energy cost and guaranteeing thermal comfort. The framework includes an electrical-analogous thermal model, the formulation of the energy optimization problem with thermal and electrical constraints. The ISO 7730 standard is used to evaluate thermal comfort. The approach is applied to the air conditioning system of a radiotherapy and medical imaging center in Argentina. The optimization problem is solved through a genetic algorithm. To evaluate the strategy, two scenarios with different power demands are proposed: Case 1 (with demands lower than 300 kW) and Case 2 (with a peak demand greater than 300 kW). The results are compared with those obtained from an on-off strategy control with hysteresis. Penalties for large demands are avoided in Case 2, and therefore an economic saving of ≅ 16.8% is achieved. The thermal comfort is improved in both cases, with thermal cost reduction of 40.6% and 29.2% for Cases 1 and 2, respectively.
dc.format
application/pdf
dc.language.iso
eng
dc.publisher
Elsevier
dc.rights
info:eu-repo/semantics/restrictedAccess
dc.rights
Atribución-NoComercial-CompartirIgual 2.5 Argentina (CC BY-NC-SA 2.5 AR)
dc.rights.uri
https://creativecommons.org/licenses/by-nc-sa/2.5/ar/
dc.subject
DEMAND RESPONSE
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DEMAND SIDE MANAGEMENT
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GENETIC ALGORITHM
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HVAC MANAGEMENT
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POWER OPTIMIZATION
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THERMAL MODEL
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Sistemas de Automatización y Control
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Ingeniería Eléctrica, Ingeniería Electrónica e Ingeniería de la Información
dc.subject.classification
INGENIERÍAS Y TECNOLOGÍAS
dc.title
Demand response strategy applied to planning the operation of an air conditioning system: Application to a medical center
dc.type
info:eu-repo/semantics/article
dc.type
info:ar-repo/semantics/artículo
dc.type
info:eu-repo/semantics/publishedVersion
dc.date.updated
2024-01-02T11:50:01Z
dc.journal.volume
57
dc.journal.pagination
1-16
dc.journal.pais
Países Bajos
dc.journal.ciudad
Amsterdam
dc.description.fil
Fil: Bragagnolo, Sergio Nicolás. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Tecnologica Nacional. Facultad Regional Cordoba. Centro de Investigacion Desarrollo y Transferencia de Ingenieria En Energia Electrica.; Argentina
dc.description.fil
Fil: Schierloh, R. M.. Universidad Tecnológica Nacional. Facultad Regional Paraná; Argentina
dc.description.fil
Fil: Vega, Jorge Ruben. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Santa Fe. Instituto de Desarrollo Tecnológico para la Industria Química. Universidad Nacional del Litoral. Instituto de Desarrollo Tecnológico para la Industria Química; Argentina
dc.description.fil
Fil: Vaschetti, Jorge Carlos. Universidad Tecnologica Nacional. Facultad Regional Cordoba. Centro de Investigacion Desarrollo y Transferencia de Ingenieria En Energia Electrica.; Argentina
dc.journal.title
Journal of Building Engineering
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/url/https://www.sciencedirect.com/science/article/pii/S235271022200938X
dc.relation.alternativeid
info:eu-repo/semantics/altIdentifier/doi/https://doi.org/10.1016/j.jobe.2022.104927
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