A Probabilistic Algorithm for Predictive Control With Full-Complexity Models in Non-Residential Buildings
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Gómez Romero, Juan; Fernández Basso, Carlos Jesús; Cambronero, M. Victoria; Molina Solana, Miguel José; Campaña Gómez, Jesús Roque; Ruiz, M. Dolores; Martín Bautista, María JoséEditorial
IEEE
Materia
Model predictive control Simulation Building energy management system Control
Date
2019-03-19Referencia bibliográfica
Gómez-Romero, J., Fernández-Basso, C. J., Cambronero, M. V., Molina-Solana, M., Campaña, J. R., Ruiz, M. D., & Martin-Bautista, M. J. (2019). A probabilistic algorithm for predictive control with full-complexity models in non-residential buildings. IEEE Access, 7, 38748-38765.
Sponsorship
This work was supported in part by the Universidad de Granada under Grant P9-2014-ING, in part by the Spanish Ministry of Science, Innovation and Universities under Grant TIN2017-91223-EXP, in part by the Spanish Ministry of Economy and Competitiveness under Grant TIN2015-64776-C3-1-R, and in part by the European Union (Energy IN TIME EeB.NMP.2013-4), under Grant 608981.Abstract
Despite the increasing capabilities of information technologies for data acquisition and processing,
building energy management systems still require manual configuration and supervision to achieve
optimal performance. Model predictive control (MPC) aims to leverage equipment control-particularly
heating, ventilation, and air conditioning (HVAC)-by using a model of the building to capture its dynamic
characteristics and to predict its response to alternative control scenarios. Usually, MPC approaches are based
on simplified linear models, which support faster computation but also present some limitations regarding
interpretability, solution diversification, and longer-term optimization. In this paper, we propose a novel
MPC algorithm that uses a full-complexity grey-box simulation model to optimize HVAC operation in
non-residential buildings. Our system generates hundreds of candidate operation plans, typically for the next
day, and evaluates them in terms of consumption and comfort by means of a parallel simulator configured
according to the expected building conditions (weather and occupancy). The system has been implemented
and tested in an office building in Helsinki, both in a simulated environment and in the real building, yielding
energy savings around 35% during the intermediate winter season and 20% in the whole winter season with
respect to the current operation of the heating equipment.