Research team
Expertise
Hossein Tabari is a scientist who works in the sustainable development domain. He tackles operational and decision-making challenges in this domain using a multidisciplinary approach that draws upon his knowledge of physical processes, statistical models, computational statistics, and data-driven learning tools. Currently, his research focuses on using machine learning to assess and mitigate the impacts of climate change on extreme events. As part of his efforts to mitigate the impacts of climate change, Tabari is promoting the use of renewable energy sources. To achieve this goal, he employs physics-informed machine learning techniques for forecasting and projections. Specifically, he uses physics-based models to provide the underlying physical constraints, while machine learning models provide the flexibility to capture complex dynamics and interactions between climatic variables. By combining these approaches, Tabari aims to develop effective solutions for mitigating the effects of climate change.
Closing the sim-to-real gap: A hybrid framework for HVAC simulation and fault detection.
Abstract
Buildings contribute to 40% of global energy consumption, with 36% attributed to heating, ventilation, and air-conditioning (HVAC). Therefore, optimizing HVAC control in buildings is crucial in the transition to a more sustainable society. Model predictive control (MPC) and (deep) reinforcement learning (DRL) have been explored for optimal control strategies, producing promising results. However, their performance depends on the underlying simulation model's accuracy, which is why an accurate model throughout the building's lifecycle is important. Physics-based models introduce discrepancies due to necessary simplifications, called the sim-to-real-gap. Closing this gap requires expert knowledge to increase the model's complexity, which is often not feasible. Given the emergence of smart, sensor-equipped buildings, data-driven solutions are possible, enabling a hybrid model that exploits the advantages of data-driven and physics-based models. First, data-driven models, like for example deep neural networks (DNNs), are added to the physics-based model on the level of the components to close the sim-to-real-gap. Second, as components degrade, the sim-to-real-gap will grow again and is closed using the same approach. Third, the hybrid model facilitates automatic fault detection and diagnosis (AFDD) using results from the adjustment process. Finally, an assessment of energy loss due to component degradation guides cost-optimal maintenance strategies.Researcher(s)
- Promoter: Hellinckx Peter
- Co-promoter: Tabari Hossein
- Co-promoter: Verhaert Ivan
- Fellow: Houben Pieter Jan
Research team(s)
Project type(s)
- Research Project
Strengthening the research capacities for extreme weather events in Romania (SCEWERO).
Abstract
The SCEWERO project will be developed by a consortium of 5 organizations from 4 countries: Babeș-Bolyai University (UBB), a research institution located in Romania as a widening country and acting as coordinator, three top-class leading partners, Fondazione Centro Euro-Mediterraneo Sui Cambiamenti Climatici (IT), Universiteit Antwerpen (BE), and Justus-Liebig-Universität Giessen (DE), and a private partner (SME), Indeco Soft (RO) aiming to improve the excellence capacity in research, to raise the scientific reputation, research profile and attractiveness through networking, and strengthening research management capacity and administrative skills of the UBB team. The SCEWERO project will be implemented in 9 work packages: one is dedicated to Ethics issues, two are dedicated to project management, and two to dissemination, exploitation, and communication. Two dedicated WPs focus on comprehensive training for UBB researchers provided by the top-class partners on the topics of i. extreme temperature and precipitation events, compound events, and artificial intelligence use for better analyzing and forecasting them (WP4) and ii. science communication on weather extremes and artificial intelligence (WP5). WP6 is dedicated to consolidating research management capacity and administrative skills and providing instruction for a dedicated working group to be created in the UBB. WP7 covers a small research project with the UBB team that holds the potential to make a significant impact. It will put into practice the knowledge transferred through instructions provided in WPs 4 and 5 by the high-performing partners. Aligned with the Early Warnings for All Initiative and the EU mission on Adaptation to Climate Change and the European Green Deal objectives, the research component aims to establish a new methodology and provide relevant results obtained through a complex approach to contribute to the enhancement of the early warning systems on heat event in Romania. The methodology could be then replicated in other European countries, paving the way for a more resilient future.Researcher(s)
- Promoter: Tabari Hossein
Research team(s)
Project type(s)
- Research Project
Industrial scale solutions improving net Stability through Flexibility (InStaFlex).
Abstract
InStaFlex explores the exploitation of deferrable loads in industries for transmission system flexibility through demand side management. We focus on optimizing actions across non-local sites, considering a diverse portfolio of industrial deferrable loads and distributed energy resources. Moreover, we investigate how to incentivize end-users, considering trade-offs and user management; and develop and exploit tools to (i) quantify the value of flexible assets (aiding end-users with negotiations with demand aggregators) and (ii) offer recommendations to policymakers.Researcher(s)
- Promoter: Hellinckx Peter
- Co-promoter: Tabari Hossein
Research team(s)
Project type(s)
- Research Project
Improving Wind and Solar Energy Forecasting Through Physics-Informed Machine Learning.
Abstract
Renewable energy sources are emerging as a crucial alternative to traditional energy sources, driven by the pressing need to reduce greenhouse gas emissions and mitigate the effects of climate change. Accurate forecasting of renewable energy resources is essential for effective decision-making in the energy sector, particularly in deeply decarbonized energy systems. Machine learning (ML) can play a significant role in improving the accuracy of renewable energy forecasting by integrating it with numerical weather prediction (NWP) models, known as physics-informed ML. This approach can address the challenge of the poor extrapolation/generalization capability of ML models by leveraging the foundation of physics-based models to generalize better to new situations. This project aims to develop a novel physics-informed ML model by integrating physical equations from NWP models with ML models to enhance the accuracy and reliability of renewable energy forecasting, focusing on wind and solar energy production forecasting. The successful implementation of this model has the potential to promote the sustainability of the energy system, lower balancing costs, and combat climate change.Researcher(s)
- Promoter: Tabari Hossein
Research team(s)
Project type(s)
- Research Project
Artifical Intelligence in Meteorological Applications (AIM).
Abstract
A main part of the mission of the RMI is to produce permanent services in order to ensure the security and the information of the population and to support the political authorities in their decision m a king. The development of numerical weather prediction mo dels (NWP) has long been a crucial part of this service. Important developments of the last years are the ever increasing amount of meteorological observations used to improve NWP forecasts through d a ta assimilation and statistical postprocessing , the use of probabilistic ensemble model s that enable better decision support , the ever increasing resolution of the models , and the incorporation of urban effects through land surface schemes . The RMI also o p erationally runs a dedicated road weather mo del since winter 2018 2019 for Belgian highways , giving decision support to traffic agencies such as Agentschap Wegen en Verkeer (AWV) in Flanders High resolution NWP models and data assimilation techniques, en s emble models and the RMI road weather model must continu e to take advantage of the newest scientific developments. Artificial intelligenceis impacting numerous scientific fields , and meteorology is no exception . For example, techniques an d software libraries from Deep Learning are being used in the field of data assimilation and neural networks are starting to be applied to statistica l postprocessing of ensemble forecasts Another important evolution is the availability of crowdsourced meteorologica l data such as from volunteer stations , and new types of sensors such as vehicle sensors, which will be tested in the RMI road weather mo del in the context of the SARWS project. Assimilation of such data can only improve model forecasts if adequate quality control is applied. An innovative new approach is the use of distributed intelligence to perform part of the necessary computations at the le vel of the sensors, before centralizing the data. It isobvious that the RMI would benefit greatly from a univer s ity partner with expertise in artificial intelligence and data science. IDLab University of Antwerp brings such expertise to the table. IDLab performs fundamental and applied research on internet technologies and data science. Within UA , the distributed intelligence group focuses on topics such as distributed and agent based intelligence, scientific machine learning, resource aware AI, and deep reinforcement learningResearcher(s)
- Promoter: Hellinckx Peter
- Fellow: Casteels Wim
- Fellow: Tabari Hossein
Research team(s)
Project type(s)
- Research Project