HVAC optimization genetic algorithm for industrial near-zero-energy building demand responseHVAC optimization genetic algorithm for industrial near-zero-energy building demand response Peer-Reviewed Journal Publication Δημοσίευση σε Περιοδικό με Κριτές 2020-06-012019enDemand 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.http://creativecommons.org/licenses/by/4.0/Energies1211Kampelis_et_al_Energ_12(11)_2019.pdfChania [Greece]Library of TUC2020-05-29application/pdf4.2 MBfree Kampelis Nikolaos Καμπελης Νικολαος Sifakis Nikolaos Σηφακης Νικολαος Kolokotsa Dionysia Κολοκοτσα Διονυσια Gobakis Konstantinos Γομπακης Κωνσταντινος Kalaitzakis Konstantinos Καλαϊτζακης Κωνσταντινος Isidori Daniela Cristalli Cristina MDPI Demand response Genetic algorithm HVAC optimization Near-zero-energy building