Ιδρυματικό Αποθετήριο
Πολυτεχνείο Κρήτης
Πολυτεχνείο Κρήτης
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| URI | http://purl.tuc.gr/dl/dias/CB77DE20-0789-4E76-A4CF-DDF4ED0F83C4 | - |
| Αναγνωριστικό | https://doi.org/10.3390/en12112177 | - |
| Αναγνωριστικό | https://www.mdpi.com/1996-1073/12/11/2177 | - |
| Γλώσσα | en | - |
| Μέγεθος | 23 pages | en |
| Τίτλος | HVAC optimization genetic algorithm for industrial near-zero-energy building demand response | en |
| Δημιουργός | Kampelis Nikolaos | en |
| Δημιουργός | Καμπελης Νικολαος | el |
| Δημιουργός | Sifakis Nikolaos | en |
| Δημιουργός | Σηφακης Νικολαος | el |
| Δημιουργός | Kolokotsa Dionysia | en |
| Δημιουργός | Κολοκοτσα Διονυσια | el |
| Δημιουργός | Gobakis Konstantinos | en |
| Δημιουργός | Γομπακης Κωνσταντινος | el |
| Δημιουργός | Kalaitzakis Konstantinos | en |
| Δημιουργός | Καλαϊτζακης Κωνσταντινος | el |
| Δημιουργός | Isidori Daniela | en |
| Δημιουργός | Cristalli Cristina | en |
| Εκδότης | MDPI | en |
| Περίληψη | Demand response offers the possibility of altering the profile of power consumption of individual buildings or building districts, i.e., microgrids, for economic return. There is significant potential of demand response in enabling flexibility via advanced grid management options, allowing higher renewable energy penetration and efficient exploitation of resources. Demand response and distributed energy resource dynamic management are gradually gaining importance as valuable assets for managing peak loads, grid balance, renewable energy source intermittency, and energy losses. In this paper, the potential for operational optimization of a heating, ventilation, and air conditioning (HVAC) system in a smart near-zero-energy industrial building is investigated with the aid of a genetic algorithm. The analysis involves a validated building energy model, a model of energy cost, and an optimization model for establishing HVAC optimum temperature set points. Optimization aims at establishing the trade-off between the minimum daily cost of energy and thermal comfort. Predicted mean vote is integrated in the objective function to ensure thermal comfort requirements are met. | en |
| Τύπος | Peer-Reviewed Journal Publication | en |
| Τύπος | Δημοσίευση σε Περιοδικό με Κριτές | el |
| Άδεια Χρήσης | http://creativecommons.org/licenses/by/4.0/ | en |
| Ημερομηνία | 2020-06-01 | - |
| Ημερομηνία Δημοσίευσης | 2019 | - |
| Θεματική Κατηγορία | Demand response | en |
| Θεματική Κατηγορία | Genetic algorithm | en |
| Θεματική Κατηγορία | HVAC optimization | en |
| Θεματική Κατηγορία | Near-zero-energy building | en |
| Βιβλιογραφική Αναφορά | N. Kampelis, N. Sifakis, D. Kolokotsa, K. Gobakis, K. Kalaitzakis, D. Isidori and C. Cristalli, "HVAC optimization genetic algorithm for industrial near-zero-energy building demand response," Energies, vol. 12, no. 11, Jun. 2019. doi: 10.3390/en12112177 | en |
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