scholarly journals Model-based Predictive Control of an HVAC System

2019 ◽  
Vol 11 (1) ◽  
pp. 101-104
Author(s):  
Tamás Kardos ◽  
Dénes Nimród Kutasi
Keyword(s):  
Predictive Control ◽  
Air Conditioning ◽  
Cooling System ◽  
Office Building ◽  
Heat Sources ◽  
Hvac System ◽  
Building Model ◽  
Model Based ◽  
Cooling Method ◽  
Model Predictor

Abstract This paper presents the application of two model-based predictive control (MPC) algorithms on the cooling system of an office building. The two strategies discussed are a simple MPC, and an adaptive MPC algorithm connected to a model predictor. The cooling method used represents the air-conditioning unit of an HVAC system. The temperature of the building’s three rooms is controlled with fan coil units, based on the reference temperature and with different constraints applied. Furthermore, the building model is affected by dynamically changing interior and exterior heat sources, which we introduced into the controller as disturbances.

scholarly journals Modelling and Model-Based Control of an HVAC System

2019 ◽  
Vol 10 (1) ◽  
pp. 25-30
Author(s):  
Tamás Kardos ◽  
Dénes Nimród Kutasi
Keyword(s):  
Air Conditioning ◽  
Room Temperature ◽  
Electrical Energy ◽  
Office Building ◽  
Heat Sources ◽  
Hvac System ◽  
Electrical Grid ◽  
Building Model ◽  
Industrial Buildings ◽  
Conditioning Equipment

Abstract An HVAC system contains heating, ventilation and air conditioning equipment used in office or industrial buildings. The goal of this research is to design a controller for the process of cooling an office building that is made up of three rooms. The desired room temperature can be achieved by controlling the fans making up the fan coil units and the cooling medium’s temperature. By these means the building connected to the electrical grid becomes a smart office. The used building model includes several dynamically changing interior and exterior heat sources affecting the inner climate, which introduces a level of uncertain prediction into the system. We have determined the controller’s performance by the rate of deviation from the expected temperature, the consumed electrical energy and the generated noise. The controller was created in Matlab Simulink with the possibility of migration to a Siemens PLC.

scholarly journals How to compare energy performance requirements of Japanese and European office buildings

2019 ◽  
Vol 111 ◽  
pp. 03038
Author(s):  
Kaiser Ahmed ◽  
Gyuyoung Yoon ◽  
Makiko Ukai ◽  
Jarek Kurnitski
Keyword(s):  
Thermal Conductance ◽  
District Heating ◽  
Energy Performance ◽  
Office Building ◽  
Heat Sources ◽  
Building Model ◽  
Air Source Heat Pump ◽  
Performance Requirements ◽  
Insulation Thickness ◽  
Heating Degree Days

This study applied the normalisation method that enabled to compare the energy performance of buildings from European and Japanese climates. A reference office building was simulated with national input data and weather file in order to estimate the thermal conductance of building model and heating degree-days for a reference climate. Based on simulated results, economic insulation thickness and thermal transmittance of windows for all climates were determined. A reference office building corresponding to Japanese ZEB Ready performance was moved with this method to Estonian and French climates. The results compared to national NZEB requirements and EC NZEB Nordic and Oceanic recommendations. It was found that the Japanese ZEB Ready building configuration with air source heat pump was very close to EC NZEB recommendations. However, in the case of district heating and gas-boiler heat sources, it was needed to improve Japanese ZEB Ready building configuration in order to meet EC NZEB recommendations. Estonian NZEB requirement met EC recommendation with both heat sources, but French NZEB requirement was much less ambitious.

scholarly journals Model Predictive Control with Adaptive Building Model for Heating Using the Hybrid Air-Conditioning System in a Railway Station

Energies ◽  
2021 ◽  
Vol 14 (7) ◽  
pp. 1996
Author(s):  
Ruixin Lv ◽  
Zhongyuan Yuan ◽  
Bo Lei ◽  
Jiacheng Zheng ◽  
Xiujing Luo
Keyword(s):  
Energy Consumption ◽  
Model Predictive Control ◽  
Thermal Comfort ◽  
Predictive Control ◽  
Pid Control ◽  
Air Conditioning ◽  
Adaptive Model ◽  
Air Conditioning System ◽  
Building Model ◽  
Indoor Thermal Comfort

A model predictive control (MPC) system with an adaptive building model based on thermal-electrical analogy for the hybrid air conditioning system using the radiant floor and all-air system for heating is proposed in this paper to solve the heating supply control difficulties of the railway station on Tibetan Plateau. The MPC controller applies an off-line method of updating the building model to improve the accuracy of predicting indoor conditions. The control performance of the adaptive MPC is compared with the proportional-integral-derivative (PID) control, as well as an MPC without adaptive model through simulation constructed based on a TRNSYS-MATLAB co-simulation testbed. The results show that the implementation of the adaptive MPC can improve indoor thermal comfort and reduce 22.2% energy consumption compared to the PID control. Compared to the MPC without adaptive model, the adaptive MPC achieves fewer violations of constraints and reduces energy consumption by 11.5% through periodic model updating. This study focuses on the design of a control system to maintain indoor thermal comfort and improve system efficiency. The proposed method could also be applied in other public buildings.

Model-based predictive control of an ice storage device in a building cooling system

2013 ◽  
Vol 111 ◽  
pp. 1032-1045 ◽  
Author(s):  
J.A. Candanedo ◽  
V.R. Dehkordi ◽  
M. Stylianou
Keyword(s):  
Predictive Control ◽  
Cooling System ◽  
Storage Device ◽  
Ice Storage ◽  
Model Based ◽  
Building Cooling

scholarly journals THE EFFECT OF CIRCULATING FAN AND NATURAL VENTILATION ON THE THERMAL COMFORT PERFORMANCE IN A BUILDING IN SOUTHEAST SULAWESI

2015 ◽  
Vol 6 (2) ◽  
Author(s):  
Aditya Rachman ◽  
Aspin Aspin ◽  
Siti Belinda
Keyword(s):  
Air Conditioning ◽  
Natural Ventilation ◽  
Cooling System ◽  
Supply And Demand ◽  
Energy Utilization ◽  
Energy Price ◽  
Building Model ◽  
Cooling Devices ◽  
Alternative Approaches

The unbalance on the supply and demand on the energy has an impact on the escalating energy price and potentially impedes the development. One of the power demands comes from the energy utilization in the building sector, in which the cooling system is among them. The application of the air conditioning based on the refrigerator system is obviously consuming much energy in the building. Thus reducing the role of this power consumed cooling system is so imperative. To apply the circulating fan or to employ the natural ventilation are among the alternative approaches to make a building cooler, without much consuming energy. The aim of this study is to investigate the effect the circulating fan and the natural ventilation, on the temperature and the humidity in a building in Southeast Sulawesi, a region with its high daily solar radiation. It conducts an experiment on a building model incorporated with the ventilation and the circulating fan. The result shows that by incorporating these alternative cooling devices, the temperatures in the building can be lower than that of a closed building (no fan and no ventilation). The magnitude in the decreasing on the temperature for the building with circulating fan is higher than that of the ventilation. Another result on the investigation shows that the relative humidity in the building with the circulating fan or the ventilation is relatively higher than that of the closed building.

Energy-efficient fuzzy model-based multivariable predictive control of a HVAC system

2014 ◽  
Vol 82 ◽  
pp. 520-533 ◽  
Author(s):  
Aleksander Preglej ◽  
Jakob Rehrl ◽  
Daniel Schwingshackl ◽  
Igor Steiner ◽  
Martin Horn ◽  
...  
Keyword(s):  
Predictive Control ◽  
Energy Efficient ◽  
Fuzzy Model ◽  
Hvac System ◽  
Model Based

scholarly journals Evaluating the Adaptability of Reinforcement Learning Based HVAC Control for Residential Houses

2020 ◽  
Vol 12 (18) ◽  
pp. 7727 ◽  
Author(s):  
Kuldeep Kurte ◽  
Jeffrey Munk ◽  
Olivera Kotevska ◽  
Kadir Amasyali ◽  
Robert Smith ◽  
...  
Keyword(s):  
Reinforcement Learning ◽  
Cost Reduction ◽  
Air Conditioning ◽  
Cost Savings ◽  
Cost Effective ◽  
Complex Behavior ◽  
Hvac System ◽  
Simulation Environment ◽  
Building Model ◽  
Hvac Control

Intelligent Heating, Ventilation, and Air Conditioning (HVAC) control using deep reinforcement learning (DRL) has recently gained a lot of attention due to its ability to optimally control the complex behavior of the HVAC system. However, more exploration is needed on understanding the adaptability challenges that the DRL agent could face during the deployment phase. Using online learning for such applications is not realistic due to the long learning period and likely poor comfort control during the learning process. Alternatively, DRL can be pre-trained using a building model prior to deployment. However, developing an accurate building model for every house and deploying a pre-trained DRL model for HVAC control would not be cost-effective. In this study, we focus on evaluating the ability of DRL-based HVAC control to provide cost savings when pre-trained on one building model and deployed on different house models with varying user comforts. We observed around 30% of cost reduction by pre-trained model over baseline when validated in a simulation environment and achieved up to 21% cost reduction when deployed in the real house. This finding provides experimental evidence that the pre-trained DRL has the potential to adapt to different house environments and comfort settings.

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