Hybrid data driven/thermal simulation model for comfort assessment

Machine learning models improve the speed and quality of physical models. However, they require a large amount of data, which is often difficult and costly to acquire. Predicting thermal comfort, for example, requires a controlled environment, with participants presenting various characteristics (age, gender, ...). This paper proposes a method for hybridizing real data with simulated data for thermal comfort prediction. The simulations are performed using Modelica Language. A benchmarking study is realized to compare different machine learning methods. Obtained results look promising with an F1 score of 0.999 obtained using the random forest model

Convection naturelle dans un canal vertical en eau avec chauffage pariétal : influence de la stratification

Pour des applications liées à l'intégration de l'énergie solaire sur les bâtiments, nous étudions la convection naturelle qui se développe dans un canal vertical ouvert en haut et en bas avec un chauffage pariétal. Le fluide est de l'eau pour s'affranchir des problèmes de rayonnement entre parois. Nous présentons des mesures expérimentales de température, de flux de chaleur et de vitesse dans le canal. Outre des corrélations globales à l'échelle du canal, nous présentons des résultats issus de mesures en deux points très proches de température et de vitesse

Etude expérimentale des écoulements de convection naturelle en canal vertical différentiellement chauffé

Ce travail porte sur l'analyse expérimentale des écoulements de convection naturelle en relation avec le comportement thermique des parois d'un canal différentiellement chauffé. Le développement de l'écoulement sur la hauteur du canal est présentée pour un nombre de Rayleigh fixé. L'influence du nombre de Rayleigh sera ensuite étudiée au travers de mesures ponctuelles de température et de vitesse. Les transferts de chaleur aux parois sont évalués et représentés sous forme de corrélations

Prospective study on the impact of electrical vehicles on the winter load peak in a village of East of France

International audienc

Indoor Air Quality (IAQ) in the IBPSA Modelica Library (part II): Methodology of integration of new IAQ simulation models

International audienceThe paper focuses on the simulation of indoor air quality (IAQ) for residential and tertiary buildings with the IBPSA modelica library. Developed methodologies for physical modeling of IAQ in the IBPSA library are first presented. Then, a case study of a room subject to external sources and internal production of pollutants, while interacting with several wall surfaces, has been assembled. Results were consistent compared to analytical and numerical solutions and simulations converged faster than a similar assembly built using the BuildSysPro-QAI modelica library. Finally, Effective Moisture Penetration Depth (EMPD) model has been integrated to evaluate the moisture interaction between walls and the air volume. Key innovations Adding functions that transform data from concentrations to mass fractions using the density of the whole mixture (medium) and the inverse of this transformation. Introducing the notion of Particulate Matter Interval (PMI) to ensure a better representation of the inert particles' diameter. Creating a new connector dedicated to the flow of air components to be used for IAQ related studies. Effective Moisture Penetration Depth (EMPD) model previously implemented in the BuildSysPro IAQ modelica library (BSP-QAI) has been added to the IBPSA library. Practical implications The open source IBPSA modelica library is required. A recent version of Dymola (2021 and later) is needed

Indoor Air Quality (IAQ) in the Modelica IBPSA Library (part I): Validation of the considered hypotheses regarding polluted air in residential and tertiary buildings

International audienceThe IBPSA Modelica library has proved to be one of the most efficient in terms of computational time for multizone thermo-hygro-aeraulic configurations. This library has additional functionalities which suggest the possibility of integrating indoor air quality (IAQ) modeling: The library considers pollutants as traces with a negligible influence on the physical properties of the mixture neglecting diffusive flows compared to advective ones since flows are described as streams. The paper validates those assumptions through two case studies evaluating the physical state and the flow of a polluted humid air with extreme concentrations of pollutants compared to common conditions in residential and tertiary buildings. Key innovations Pollutants can be considered as traces with a negligible influence on the physical state of the mixture. The influence of water vapor on the physical state of the mixture is non negligible. The diffusive flow of pollutants in air is negligible compared to its advective flow when the air is in motion. As a result of the previous points, IAQ modeling in residential and tertiary buildings is possible with the IBPSA modelica library. Practical implications In the case of IAQ modeling by the IBPSA modelica library in other contexts, e.g. in industrial buildings with high concentrations of pollutants, the user will always have to check that the assumptions discussed in this paper are verified for the evaluated case study

Electrical Grid Flexibility via Heat Pump and Thermal Storage Control

International audienceDistrict Heating Networks (DHN) that include heat pumps as a heating source can offer flexibility to electrical grids. These systems are now used as part of smart city development where the electrical consumption of heat pumps can be controlled. They respond to the Demand-Side Management (DSM) programs and change their consumption to satisfy the constraints on the electrical grid while respecting the end-user heating demand. When thermal storage is included in DHN, there is an increase in the flexibility offered and a shift to using less fossil fuel energy. This paper proposes a heat pump and thermal storage system for space and water heating of twelve tertiary buildings. The proposed DHN system is modeled using Dymola, and is controlled via MATLAB to respond to flexibility demands which are given at the electrical grid level by the Distribution System Operator (DSO). A Model Predictive Controller (MPC) regulates the electrical consumption of the heat pump to perform peak shaving or valley filling for the electrical grid, or to minimize the electrical energy consumed over a 24-hour time window. This paper also presents an optimization problem that responds to different programs using only the heat pump and the thermal storage to satisfy end-user demands. Results show that thermal storage is used more often to satisfy the heating demands of the buildings when the control is activated. The system should be notified early enough before the peak shaving demand so it would be possible to turn off the heat pump

Electrical Grid Flexibility via Heat Pump and Thermal Storage Control

International audienceDistrict Heating Networks (DHN) that include heat pumps as a heating source can offer flexibility to electrical grids. These systems are now used as part of smart city development where the electrical consumption of heat pumps can be controlled. They respond to the Demand-Side Management (DSM) programs and change their consumption to satisfy the constraints on the electrical grid while respecting the end-user heating demand. When thermal storage is included in DHN, there is an increase in the flexibility offered and a shift to using less fossil fuel energy. This paper proposes a heat pump and thermal storage system for space and water heating of twelve tertiary buildings. The proposed DHN system is modeled using Dymola, and is controlled via MATLAB to respond to flexibility demands which are given at the electrical grid level by the Distribution System Operator (DSO). A Model Predictive Controller (MPC) regulates the electrical consumption of the heat pump to perform peak shaving or valley filling for the electrical grid, or to minimize the electrical energy consumed over a 24-hour time window. This paper also presents an optimization problem that responds to different programs using only the heat pump and the thermal storage to satisfy end-user demands. Results show that thermal storage is used more often to satisfy the heating demands of the buildings when the control is activated. The system should be notified early enough before the peak shaving demand so it would be possible to turn off the heat pump

Electrical Grid Flexibility via Heat Pump and Thermal Storage Control

International audienceDistrict Heating Networks (DHN) that include heat pumps as a heating source can offer flexibility to electrical grids. These systems are now used as part of smart city development where the electrical consumption of heat pumps can be controlled. They respond to the Demand-Side Management (DSM) programs and change their consumption to satisfy the constraints on the electrical grid while respecting the end-user heating demand. When thermal storage is included in DHN, there is an increase in the flexibility offered and a shift to using less fossil fuel energy. This paper proposes a heat pump and thermal storage system for space and water heating of twelve tertiary buildings. The proposed DHN system is modeled using Dymola, and is controlled via MATLAB to respond to flexibility demands which are given at the electrical grid level by the Distribution System Operator (DSO). A Model Predictive Controller (MPC) regulates the electrical consumption of the heat pump to perform peak shaving or valley filling for the electrical grid, or to minimize the electrical energy consumed over a 24-hour time window. This paper also presents an optimization problem that responds to different programs using only the heat pump and the thermal storage to satisfy end-user demands. Results show that thermal storage is used more often to satisfy the heating demands of the buildings when the control is activated. The system should be notified early enough before the peak shaving demand so it would be possible to turn off the heat pump