Hossein Tabari assistant professor - senior researcher

Research Hossein Tabari

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.

Improving Wind and Solar Energy Forecasting Through Physics-Informed Machine Learning. 01/06/2024 - 31/05/2028

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)

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Project type(s)

  • Research Project

Artifical Intelligence in Meteorological Applications (AIM). 01/09/2021 - 31/08/2031

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 learning

Researcher(s)

Research team(s)

Project type(s)

  • Research Project