scholarly journals An Innovative Modelling Approach Based on Building Physics and Machine Learning for the Prediction of Indoor Thermal Comfort in an Office Building

2021 ◽  
Vol 11 (1) ◽  
pp. 25
Author(s):  
Giovanni Tardioli ◽  
Ricardo Filho ◽  
Pierre Bernaud ◽  
Dimitrios Ntimos
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Dynamic Simulation ◽  
Energy Model ◽  
Environmental Data ◽  
Office Building ◽  
Environmental Sensors ◽  
Virtual Sensors ◽  
Indoor Thermal Comfort ◽  
Dynamic Simulation Model

In this paper, an innovative hybrid modelling technique based on machine learning and building dynamic simulation is presented for the prediction of indoor thermal comfort feedback from occupants in an office building in Le Bourget-du-Lac, Chambéry, France. The office was equipped with Internet of Things (IoT) environmental sensors. A calibrated building energy model was created for the building using optimisation tools. Thermal comfort was collected using a portable device. A machine learning (ML) model was trained using collected feedback, environmental data from IoT devices and synthetic datasets (virtual sensors) extracted from a physics-based model. A calibrated energy model was used in co-simulation with the predictive method to estimate comfort levels for the building. The results show the ability of the method to improve the prediction of occupant feedback when compared to traditional thermal comfort approaches of about 25%, the importance of information extracted from the physics-based model and the possibility of leveraging scenario evaluation capabilities of the dynamic simulation model for control purposes.

scholarly journals BIM and Data-Driven Predictive Analysis of Optimum Thermal Comfort for Indoor Environment

Sensors ◽  
2021 ◽  
Vol 21 (13) ◽  
pp. 4401
Author(s):  
Vincent J.L. Gan ◽  
Han Luo ◽  
Yi Tan ◽  
Min Deng ◽  
H.L. Kwok
Keyword(s):  
Machine Learning ◽  
Mechanical Ventilation ◽  
Boundary Conditions ◽  
Thermal Comfort ◽  
Indoor Environment ◽  
Natural Ventilation ◽  
Data Driven ◽  
Predictive Analysis ◽  
Comfort Index ◽  
Indoor Thermal Comfort

Mechanical ventilation comprises a significant proportion of the total energy consumed in buildings. Sufficient natural ventilation in buildings is critical in reducing the energy consumption of mechanical ventilation while maintaining a comfortable indoor environment for occupants. In this paper, a new computerized framework based on building information modelling (BIM) and machine learning data-driven models is presented to analyze the optimum thermal comfort for indoor environments with the effect of natural ventilation. BIM provides geometrical and semantic information of the built environment, which are leveraged for setting the computational domain and boundary conditions of computational fluid dynamics (CFD) simulation. CFD modelling is conducted to obtain the flow field and temperature distribution, the results of which determine the thermal comfort index in a ventilated environment. BIM–CFD provides spatial data, boundary conditions, indoor environmental parameters, and the thermal comfort index for machine learning to construct robust data-driven models to empower the predictive analysis. In the neural network, the adjacency matrix in the field of graph theory is used to represent the spatial features (such as zone adjacency and connectivity) and incorporate the potential impact of interzonal airflow in thermal comfort analysis. The results of a case study indicate that utilizing natural ventilation can save cooling power consumption, but it may not be sufficient to fulfil all the thermal comfort criteria. The performance of natural ventilation at different seasons should be considered to identify the period when both air conditioning energy use and indoor thermal comfort are achieved. With the proposed new framework, thermal comfort prediction can be examined more efficiently to study different design options, operating scenarios, and changeover strategies between various ventilation modes, such as better spatial HVAC system designs, specific room-based real-time HVAC control, and other potential applications to maximize indoor thermal comfort.

scholarly journals Effectiveness of Automatic and Manual Calibration of an Office Building Energy Model

2019 ◽  
Vol 9 (10) ◽  
pp. 1985 ◽  
Author(s):  
Cristina Cornaro ◽  
Francesco Bosco ◽  
Marco Lauria ◽  
Valerio Adoo Puggioni ◽  
Livio De Santoli
Keyword(s):  
Dynamic Simulation ◽  
Simulated Data ◽  
Simulation Models ◽  
Calibration Method ◽  
Calibration Procedure ◽  
Office Building ◽  
Automatic Calibration ◽  
Advantages And Disadvantages ◽  
Dynamic Simulation Model ◽  
The Difference

Energy reduction can benefit from the improvement of energy efficiency in buildings. For this purpose, simulation models can be used both as diagnostic and prognostic tools, reproducing the behaviour of the real building as accurately as possible. High modelling accuracy can be achieved only through calibration. Two approaches can be adopted—manual or automatic. Manual calibration consists of an iterative trial and error procedure that requires high skill and expertise of the modeler. Automatic calibration relies on mathematical and statistical methods that mostly use optimization algorithms to minimize the difference between measured and simulated data. This paper aims to compare a manual calibration procedure with an automatic calibration method developed by the authors, coupling dynamic simulation, sensitivity analysis and automatic optimization using IDA ICE, Matlab and GenOpt respectively. Differences, advantages and disadvantages are evidenced applying both methods to a dynamic simulation model of a real office building in Rome, Italy. Although both methods require high expertise from operators and showed good results in terms of accuracy, automatic calibration presents better performance and consistently helps with speeding up the procedure.

Investigating the Impact of Ornamental Plants Correlated with Indoor Thermal Comfort and Eco-Energy

2017 ◽  
Vol 8 (5) ◽  
pp. 221
Author(s):  
Sugiono Sugiono ◽  
Suluh E. Swara ◽  
Wisnu Wijanarko ◽  
Dwi H. Sulistyarini
Keyword(s):  
Thermal Comfort ◽  
Ornamental Plants ◽  
Indoor Thermal Comfort ◽  
The Impact

scholarly journals Study on improving three-dimensional dynamic simulation model and equation of flexible fabric

2019 ◽  
Vol 11 (6) ◽  
pp. 168781401985284
Author(s):  
Meiliang Wang ◽  
Mingjun Wang ◽  
Xiaobo Li
Keyword(s):  
Simulation Model ◽  
Dynamic Simulation ◽  
Material Properties ◽  
Three Dimensional ◽  
Equation Solving ◽  
Study Results ◽  
Essential Properties ◽  
Dynamic Simulation Model ◽  
Novel Model ◽  
Simulation Equation

The use of the traditional fabric simulation model evidently shows that it cannot accurately reflect the material properties of the real fabric. This is against the background that the simulation result is artificial or an imitation, which leads to a low simulation equation. In order to solve such problems from occurring, there is need for a novel model that is designed to enhance the essential properties required for a flexible fabric, the simulation effect of the fabric, and the efficiency of simulation equation solving. Therefore, the improvement study results will offer a meaningful and practical understanding within the field of garment automation design, three-dimensional animation, virtual fitting to mention but a few.

scholarly journals Impacts on Indoor Thermal Comfort and Heating Energy Use in Hellenic Dwellings from Occupant Behavioral Reactions

2021 ◽  
Vol 11 (14) ◽  
pp. 6254
Author(s):  
Elena G. Dascalaki ◽  
Constantinos A. Balaras
Keyword(s):  
Thermal Comfort ◽  
Energy Use ◽  
Energy Savings ◽  
Local Heating ◽  
Economic Recession ◽  
Heating System ◽  
Building Stock ◽  
Heating Systems ◽  
Indoor Thermal Comfort ◽  
Energy Audits

In an effort to reduce the operational cost of their dwellings, occupants may even have to sacrifice their indoor thermal comfort conditions. Following the economic recession in Greece over recent years, homeowners have been forced to adapt their practices by shortening heating hours, lowering the indoor thermostat settings, isolating spaces that are not heated or even turning off their central heating system and using alternative local heating systems. This paper presents the results from over 100 occupant surveys using questionnaires and walk-through energy audits in Hellenic households that documented how occupants operated the heating systems in their dwellings and the resulting indoor thermal comfort conditions and actual energy use. The results indicate that the perceived winter thermal comfort conditions were satisfactory in only half of the dwellings, since the actual operating space heating periods averaged only 5 h (compared with the assumed 18 h in standard conditions), while less than half heated their entire dwellings and only a fifth maintained an indoor setpoint temperature of 20 °C, corresponding to standard comfort conditions. Mainstream energy conservation measures include system maintenance, switching to more efficient systems, reducing heat losses and installing controls. This information is then used to derive empirical adaptation factors for bridging the gap between the calculated and actual energy use, making more realistic estimates of the expected energy savings following building renovations, setting prudent targets for energy efficiency and developing effective plans toward a decarbonized building stock.

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