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Open AccessArticle

HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response

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Energy Management in the Built Environment Research Lab, School of Environmental Engineering, Technical University of Crete, Technical University Campus, Kounoupidiana, GR 73100 Chania, Greece
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Renewable and Sustainable Energy Systems Laboratory, School of Environmental Engineering, Technical University of Crete, Kounoupidiana, GR 73100 Chania, Greece
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Electric Circuits and Renewable Energy Sources Laboratory, Technical University of Crete; Kounoupidiana, GR 73100 Chania, Greece
4
Research for Innovation, AEA srl, Angeli di Rosora, 60030 Marche, Italy
*
Author to whom correspondence should be addressed.
Energies 2019, 12(11), 2177; https://doi.org/10.3390/en12112177
Received: 30 April 2019 / Revised: 3 June 2019 / Accepted: 4 June 2019 / Published: 7 June 2019
(This article belongs to the Special Issue Intelligent Control in Energy Systems Ⅱ)
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. View Full-Text
Keywords: HVAC optimization; demand response; near-zero-energy building; genetic algorithm HVAC optimization; demand response; near-zero-energy building; genetic algorithm
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Kampelis, N.; Sifakis, N.; Kolokotsa, D.; Gobakis, K.; Kalaitzakis, K.; Isidori, D.; Cristalli, C. HVAC Optimization Genetic Algorithm for Industrial Near-Zero-Energy Building Demand Response. Energies 2019, 12, 2177.

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