Contact Information
Name: Guang
Surname: Hu
Organization: Energy Technology Group, Department of Mechanical Engineering, Eindhoven University of Technology
Email: g.hu@tue.nl
Participation Terms
I consent to sharing the data and/or models in scientific publications and public reports.
Data Request/Proposal
I am seeking collaboration on residential PV data, with a specific interest in leveraging datasets related to residential solar energy performance and household energy consumption. My expertise lies in applying machine learning and multiscale modeling techniques to analyze energy data for pattern discovery, prediction, and optimization.
In exchange, I am able to provide analysis results, predictive modeling capabilities, and insight into energy efficiency improvements based on data-driven findings. I am particularly interested in obtaining data in CSV or HDF5 formats to facilitate integration with my existing models.
Collaboration Proposal
As part of my role, I would like to propose a collaboration with Serendi-PV partners and formally request access to datasets related to residential PV systems. My expertise in machine learning and multiscale modeling will help unlock valuable insights from the data, such as predicting energy output based on local environmental factors and optimizing system performance to improve energy efficiency.
My intention is to apply advanced data analytics and machine learning methods to discover meaningful patterns and opportunities in residential PV performance that can ultimately support sustainable energy adoption and more efficient grid integration. I believe this approach can benefit ongoing research activities conducted by Serendi-PV partners, especially in the context of residential energy management.
To facilitate the collaboration, I am available for regular progress updates, feedback sessions, and exchange of preliminary findings. Additionally, I would welcome any opportunity to work closely with other researchers and partners interested in residential PV system research. Please refer to my Google Scholar and official TU/e profiles for further information regarding my research background and previous work.
Timeline
I propose an initial timeline of 6 months for this collaboration, focusing on data analysis and model development during this phase. This period will also include iterative feedback and discussions with Serendi-PV partners to ensure our efforts are aligned with the overarching project goals.
Summary (to be published online on the website)
We are seeking collaboration with Serendi-PV partners to analyze residential PV datasets using advanced machine learning techniques. Our goal is to derive valuable insights into the performance of residential PV systems, focusing on predictive modeling and efficiency optimization. The proposed collaboration will run for an initial period of 6 months, during which we will work closely with partners to develop and refine models and share knowledge. For further information, please refer to my Google Scholar and TU/e profiles. We look forward to a productive and impactful collaboration.
Additional Content and Comments
No additional content at this time.
Organization Name: Eindhoven University of Technology
Name: Guang Hu
Title: Assistant Professor
Date: October 8, 2024