Yuhao Nie

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I am a Michael Hammer Postdoctoral Fellow at MIT Institute for Data, System, and Society. I am also part of the Laboratory for Information and Decision Systems and the Earth Intelligence Lab.

Previously, I received my Ph.D. in Energy Science and Engineering from Stanford University in 2023 and my M.A.Sc in Chemical Engineering from the University of British Columbia in 2018. I obtained my B.Eng. in Environmental Engineering from Harbin Institute of Technology in 2015.

My research lies at the interface of energy systems, sustainability, and artificial intelligence. I use novel data and computational algorithms to study the interplay between energy and the environment. Specifically, I focus on understanding

  • how environmnetal factors, particularly weather and climate, influence energy systems. This line of research leverages multimodal data (e.g., remote sensing imagery, weather and climate observations) and advanced machine learning methods to assess renewable resource availability, forecast energy generation, and further optimize the design and operation of more reliable and resilient energy systems.

  • how energy systems, in turn, impact the environment. This research thread combines system modeling with life cycle analysis to quantify the environmental and socioeconomic impacts of emerging energy technologies and services. It also employs remote sensing and machine learning to monitor the energy transition, e.g., mapping energy facilities, tracking energy emissions, to ensure that energy deployments align with sustainable development goals.

news

Oct 01, 2024 I will be at AGU in December to present my project “Mapping flooding in paddy fields for estimating rice methane emissions in Ghana using remote sensing and machine learning”. Feel free to reach out if you want to have a chat!
Sep 01, 2024 I received a 1-year funding from Eni to lead the development of a foundation model for solar energy meteorology!
Jul 05, 2024 I am invited for a talk at the 1st International Conference of Net Zero Carbon Built Environment. My talk would cover potential applications of sky images for smart building modeling.
Jul 02, 2024 I am invited for a talk at the Energy Visions Seminar hosted by Applied Energy Innovation Institute. My talk would cover the topic of Synthetic Sky Images from Generative Models for Probabilistic Solar Forecasting. This talk is based on the paper we recently published in Advances in Applied Energy.

selected publications

  1. skygpt-demo.gif
    SkyGPT: Probabilistic ultra-short-term solar forecasting using synthetic sky images from physics-constrained VideoGPT
    Yuhao Nie, Eric Zelikman , Andea Scott , and 2 more authors
    Advances in Applied Energy, 2024
  2. open_sky_img_datasets_survey.png
    Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey
    Yuhao Nie, Xiatong Li , Quentin Paletta , and 3 more authors
    Renewable and Sustainable Energy Reviews, 2024
  3. sky_condition_model_demo.png
    PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model
    Yuhao Nie, Yuchi Sun , Yuanlei Chen , and 2 more authors
    Journal of Renewable and Sustainable Energy, 2020
  4. LNG_LCA.png
    Greenhouse-gas emissions of Canadian liquefied natural gas for use in China: Comparison and synthesis of three independent life cycle assessments
    Yuhao Nie, Siduo Zhang , Ryan Edward Liu , and 7 more authors
    Journal of Cleaner Production, 2020
  5. HTL_LCA.png
    Life-cycle assessment of transportation biofuels from hydrothermal liquefaction of forest residues in British Columbia
    Yuhao Nie, and Xiaotao Bi
    Biotechnology for biofuels, 2018