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Commande prédictive multisystème pour l’automatisation et la régulation multizones des bâtiments.

Multi-system model predictive control for multi-zone building automation and control.

Numéro : 3378

Auteurs : SHAKYA P., BASKARAN K., SREENIVASAN S., DHARMALINGAM Y., DUBEY S., YANG S., WAN M. P.

Résumé

In this paper, a multi-zone Model Predictive Control (MPC) system with coordination between multiple systems including Air Conditioning and Mechanical Ventilation (ACMV), lighting, and shading has been presented. MPC system includes data-driven models for forward prediction of thermal conditions, illumination, and artificial lighting power for each test spaces. The thermal prediction model was trained with historical data from humidity, indoor air and globe temperature sensors, and disturbances including weather parameters and occupancy. The disturbance data were from machine-learning-based weather forecasting and a real-time occupancy detection system. Illumination and artificial lighting power prediction models were trained with historical data of lighting power meters, blind positions, and indoor illumination levels collected via sensors. Nonlinear autoregressive exogenous model with external inputs (NARX) neural network was employed to develop these data-driven models. With these data-driven models, MPC system simultaneously optimizes multiple targets including energy consumption, thermal and visual comfort by solving nonlinear optimization problems. In the case of thermal comfort, the predicted mean vote (PMV) was optimized to seek a default reference PMV setpoint of 0 representing thermal neutrality while constrained to within -0.5 to 0.5. Similarly for optimum visual comfort, daylight glare probability (DGP) should be within 0.35 and the work-plane illuminance was constrained to 500-3000 lux. An algorithm to take in occupant feedback as an additional consideration was also incorporated into the MPC system. As per occupant feedback, their preferences can bias the reference PMV set point. MPC system was implemented in a multi-use test space located in Singapore with area of approximately 850 m2. The test space was partitioned into 6 learning zones, 2 office spaces, and 3 open spaces. ACMV system serving the test space consisted of 2 Primary Air-Handling Units (PAHUs) and 16 Fan Coil Units (FCUs). Chilled water is supplied to cooling coils of these units from central chiller plant of the building. Conditioned air is distributed to test space through motorized diffusers. Lighting and shading system consisted of LED lighting fixtures with dimmable control and motorized roller blinds respectively. Control performance of MPC system was compared against the test building’s original thermostat-based (reactive) control. MPC system (without occupant feedback) achieved over 33% energy savings with higher thermal and visual comfort. When occupant feedback was considered, it was found that the occupants preferred a thermal environment cooler than thermal neutrality (i.e., negative PMV) for certain periods of the day (e.g., when the occupants have just arrived at the space in the morning or after lunch). This led to higher energy consumption compared to MPC without occupant feedback. Overall, MPC with occupant feedback still achieved over 23.5% of energy savings when compared to that of original reactive control system. Despite advantageous control performance, MPC system requires additional sensors for occupancy comfort evaluations, leading to high implementation cost. Much effort is also needed to construct accurate building models, which further adds hurdles for MPC adaptation. In future deployments of MPC, these shortcomings can be mitigated by building comfort models only based on existing sensors in the building.

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Pages : 10 p.

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Détails

  • Titre original : Multi-system model predictive control for multi-zone building automation and control.
  • Identifiant de la fiche : 30032938
  • Langues : Anglais
  • Source : 2024 Purdue Conferences. 8th International High Performance Buildings Conference at Purdue.
  • Date d'édition : 15/07/2024

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