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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  
dc.subject
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  
dc.subject.classification
Sistemas de Automatización y Control  
dc.subject.classification
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