scholarly journals Energy performance optimization in buildings: A review on semantic interoperability, fault detection, and predictive control

2018 ◽  
Vol 5 (4) ◽  
pp. 041501 ◽  
Author(s):  
Gesa A. Benndorf ◽  
Dominik Wystrcil ◽  
Nicolas Réhault
Keyword(s):  
Fault Detection ◽  
Predictive Control ◽  
Performance Optimization ◽  
Energy Performance ◽  
Semantic Interoperability

scholarly journals A New Model Predictive Control Method for Eliminating Hydraulic Oscillation and Dynamic Hydraulic Imbalance in a Complex Chilled Water System

Energies ◽  
2021 ◽  
Vol 14 (12) ◽  
pp. 3608
Author(s):  
Yang Yuan ◽  
Neng Zhu ◽  
Haizhu Zhou ◽  
Hai Wang
Keyword(s):  
Model Predictive Control ◽  
Predictive Control ◽  
Control Method ◽  
Water System ◽  
Energy Performance ◽  
Effective Control ◽  
New Model ◽  
Chilled Water ◽  
Chilled Water System ◽  
Hydraulic Oscillation

To enhance the energy performance of a central air-conditioning system, an effective control method for the chilled water system is always essential. However, it is a real challenge to distribute exact cooling energy to multiple terminal units in different floors via a complex chilled water network. To mitigate hydraulic imbalance in a complex chilled water system, many throttle valves and variable-speed pumps are installed, which are usually regulated by PID-based controllers. Due to the severe hydraulic coupling among the valves and pumps, the hydraulic oscillation phenomena often occur while using those feedback-based controllers. Based on a data-calibrated water distribution model which can accurately predict the hydraulic behaviors of a chilled water system, a new Model Predictive Control (MPC) method is proposed in this study. The proposed method is validated by a real-life chilled water system in a 22-floor hotel. By the proposed method, the valves and pumps can be regulated safely without any hydraulic oscillations. Simultaneously, the hydraulic imbalance among different floors is also eliminated, which can save 23.3% electricity consumption of the pumps.

scholarly journals Find the Gap: Project MOEEBIUS, a Holistic Energy Performance Optimization Framework

Proceedings ◽  
2017 ◽  
Vol 1 (7) ◽  
pp. 1102
Author(s):  
Rui Martins ◽  
Filipe Silva ◽  
Muriel Iten ◽  
Ricardo Rato
Keyword(s):  
Performance Optimization ◽  
Energy Performance ◽  
Optimization Framework ◽  
Gap Project

scholarly journals Model Predictive Control Optimization via Genetic Algorithm Using a Detailed Building Energy Model

Energies ◽  
2018 ◽  
Vol 12 (1) ◽  
pp. 34 ◽  
Author(s):  
Germán Ramos Ruiz ◽  
Eva Lucas Segarra ◽  
Carlos Fernández Bandera
Keyword(s):  
Genetic Algorithm ◽  
Model Predictive Control ◽  
Predictive Control ◽  
Search Space ◽  
Building Energy ◽  
Energy Performance ◽  
Hvac Systems ◽  
Convergence Time ◽  
Computational Time ◽  
Nsga Ii

There is growing concern about how to mitigate climate change in which the reduction of CO2 emissions plays an important role. Buildings have gained attention in recent years since they are responsible for around 30% of greenhouse gases. In this context, advance control strategies to optimize HVAC systems are necessary because they can provide significant energy savings whilst maintaining indoor thermal comfort. Simulation-based model predictive control (MPC) procedures allow an increase in building energy performance through the smart control of HVAC systems. The paper presents a methodology that overcomes one of the critical issues in using detailed building energy models in MPC optimizations—computational time. Through a case study, the methodology explains how to resolve this issue. Three main novel approaches are developed: a reduction in the search space for the genetic algorithm (NSGA-II) thanks to the use of the curve of free oscillation; a reduction in convergence time based on a process of two linked stages; and, finally, a methodology to measure, in a combined way, the temporal convergence of the algorithm and the precision of the obtained solution.

Open-Switch Fault Detection in Five-Phase Induction Motor Drives Using Model Predictive Control

2018 ◽  
Vol 65 (4) ◽  
pp. 3045-3055 ◽  
Author(s):  
Ignacio Gonzalez-Prieto ◽  
Mario J. Duran ◽  
Natalia Rios-Garcia ◽  
Federico Barrero ◽  
Cristina Martin
Keyword(s):  
Fault Detection ◽  
Model Predictive Control ◽  
Induction Motor ◽  
Predictive Control ◽  
Motor Drives ◽  
Induction Motor Drives ◽  
Open Switch
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