Google Summer of Code 2025: Open Data PVNet Discussion Thread

[peterdudfield]

This space is for you to ask any questions you have about this project. We're here to provide clarifications and help you understand the project's goals, scope, and requirements. Feel free to ask about anything that interests you!

Please note that this discussion is for questions and clarifications, not for formal applications.

Project Description

We're building an open-source solar forecasting pipeline to integrate with OCF's PVNet model, using publicly available Numerical Weather Prediction data to forecast solar generation at the national level, starting with the UK. Currently, our main forecasting tool, Quartz Solar, is trained using a mixture of public and private datasets, and we want to create an effective model that uses 100% open data.

The data is ready to go, but we need a ML engineer to train the model. The aim will be to start with a UK forecast, but then extend to different countries.

Expected Outcome

A UK ML Solar forecast
trained on free NWP data with the accuracy benchmarked.

Other Key Information

• Expected Size: 170hrs
• Skills: ML knowledge, Pytorch, Python. NWP knowledge is a bonus
• Difficulty level: Medium
• Related Reading: https://github.com/openclimatefix/open-data-pvnet
• Potential mentors: @peterdudfield, @Sukh-P

[aayushyatiwari]

Hey!
I like what you guys are doing here. I'd like to contribute and learn. What type of model architecture are we looking at with regards to the open data model? Where should I go to, to understand the previous model and the accuracies it produced?

[peterdudfield]

Hi @aayushyatiwari , thanks for getting in contact. This repo has some good information for gettin started - https://github.com/openclimatefix/open-data-pvnet. There are some issues in there.

We've typically used PVnet and we'll be able to share some validation results soon.

[peach280]

I am interested in contributing to Open Climate fix for gsoc 2025.
I have a couple of questions regarding this project idea.

I'm eager to apply my Python and machine learning fundamentals. How much guidance will be available for the NWP specific aspects?
How much of the work will be focused on model building, versus data preprocessing?
How much of the work will require cloud computing knowledge?"

[peterdudfield]

1. We can provide guideance for getting new NWP data. We atcually have 2 NWPs almost ready to go, so it'll probably not focusing on that.
2. Hopefully lots of model building
3. We' hoping this can be done locally. The NWP data will be on the cloud, but hopefully accessible in a zarr format, so you dont have to download it all at once

[crs7617]

Hi, @peterdudfield

I'm Sairam, and I have a strong passion for machine learning, particularly with Python and LLMs. The Open Data Solar Forecasting Pipeline project has really caught my attention, and I'm excited about the possibility of contributing to it as part of GSoC '25.

I've begun looking into the PVNet repository to familiarize myself with the current work, and I've also checked out some "good first issues." Recently, I commented on an issue in the Analysis Dashboard repository and am now waiting to be assigned so I can make my first contribution to Open Climate Fix.

In addition to training the model using open NWP data, I would like to clarify what specific contributions you are looking for from me. Should I concentrate on enhancing model performance, integrating new datasets, or is there another area you’d prefer I focus on?

Moreover, since PVNet is a key component of this project and has piqued my interest, would it be more beneficial for me to tackle a "good first issue" within PVNet instead of working on another repository? If that's the case, do you have any suggestions for an issue that would align well with the project's goals?

I'm eager to begin with an initial contribution that supports the long-term objectives.

I look forward to your guidance!

[saurabh-3110]

Hey!!

I am highly interested in the intersection of climate science, machine learning, and sustainability, and I find the work being done here particularly intriguing. I have experience working with meteorological variables and applying machine learning techniques, and I would love to contribute to the ML modeling efforts.

After reviewing the repository, I understand that the initial focus is on predicting the target variable for the entire UK region. Please correct me if I am mistaken. I would appreciate any guidance on how I can best contribute to the project.

[vijayabhaskar78]

Hi @peterdudfield,

I'm S Vijaya Bhaskar, a machine learning enthusiast with strong Python and PyTorch skills, and I have a keen interest in climate data and sustainable tech. I'm really excited about contributing to the solar forecasting project. I'm currently also contributing to OpenClimateFix in the elexonapi, so I'm getting familiar with the ecosystem. I noticed that while there aren't any issues labeled as "good first issues" at the moment, there are several open issues. Could you recommend one that would be a great starting point for someone with my background? I'm eager to dive in—whether it's improving model performance, integrating new datasets, or any other area where support is needed.

Thanks for your guidance!

[ScarWar]

Hi! This GSoC 2025 project on transitioning PVNet to a public-data-only model is very intriguing. I've been following the advancements in Numerical Weather Prediction (NWP), particularly the application of diffusion models, hybrid models, and transformers, and I'm excited to see this applied to PVNet.

I understand the core objective is to migrate PVNet from a mixed public/private dataset to a purely public dataset. This raises several key questions, and I'd love to gain a clearer understanding of the challenges:

• Performance Delta: How significant is the performance degradation observed when switching from the mixed dataset to a public-only dataset? Quantifying this delta would help define the scope of improvement needed.
• Previous Public-Only Attempts: Has PVNet been trained solely on public data before, and if so, what were the limitations encountered? Understanding past attempts can guide future strategies.
• Specific Challenges with Public Data: Are there specific characteristics of the public dataset that make it challenging for PVNet? (e.g., data sparsity, lower resolution, limited temporal coverage).
• Focus Areas: Beyond dataset migration, what are the primary focus areas for improvement? Are we aiming to:
  • Enhance model architecture to compensate for the reduced data richness?
  • Optimize training strategies to maximize the information extracted from public data?
  • Improve inference efficiency for practical deployment?
  • Address specific forecasting skill score metrics?
• Evaluation Metrics: Which evaluation metrics are most critical for this project? (e.g. RMSE, MAE, CSI, etc.)

I'm particularly interested in exploring potential architectural modifications

Thank you for your time, and I look forward to learning more about this project!

[peterdudfield]

performance delta: We will find out
previous: not yet
specific challenges: not yet
focus area: lots of those things can be done
eval: MAE is the most used, but there are others

[SophiaLi20]

Hey ,
This project sounds super interesting! I have experience with Python and Machine Learning, and I’ve been diving into PyTorch recently, so I’d love to learn more about how the model will be trained and evaluated.
I had a few questions to better understand the scope:

• Is there a specific baseline model you’d like to compare against for benchmarking accuracy?
• Since the focus is on using 100% open data, are there any known challenges with public NWP datasets—like missing values or preprocessing steps—that we should be mindful of?
• Will this model be directly integrated into PVNet, or will it start as a separate module?
• What key metrics will you be using to measure forecast accuracy?

I checked out the repo, and it looks really cool! Looking forward to learning more and getting involved.

Best,
Unnati Bhardwaj

[peterdudfield]

1. We can provide our numbers of PVnet at some point, but other benchmarks can eb done
2. not so far
3. separate module, i expect it to import PVNet
4. MAE

[ajitashwath]

Hi @peterdudfield

I'm Ajit Ashwath, and I’m really excited about this project. I’ve been working with ML for a while, mainly using Python, PyTorch and TensorFlow, and the idea of applying ML to renewable energy is really interesting to work on. I’d love to contribute to improving solar forecasting with open data. Before that, I do have some questions to ask.

Since this model will be trained solely on a public NWP dataset, are there any known issues or limitations when using it? Does it involve a lot of preprocessing we need to consider?

And, are there any particular, specific datasets available for expansion outside of the UK?

Looking forward to hearing more!

[peterdudfield]

yea, it does invlove lots of preprocessing, see https://github.com/openclimatefix/open-data-pvnet project.
Yep, see https://github.com/openclimatefix/global-solar-dataset

[Jiya873]

Hi @peterdudfield,

I'm Jiya Gupta, and I'm interested in contributing to this project and would love to get involved. Could you guide me on where to start? I'm particularly keen on understanding how the data pipeline is set up and how I can help in making the data samples more manageable for ML training.

Looking forward to your guidance!

[yeswanth244]

Hey @peterdudfield ,

I'm Yeswanth, and I'm really excited about contributing to the Open Data PVNet project as part of GSoC 2025. I have a strong background in Python and machine learning, and I'm eager to build the model from scratch using open Numerical Weather Prediction (NWP) data.

I've already started exploring the PVNet repo to understand the existing framework and get familiar with how things are structured. Before diving in, I wanted to clarify a few things about the scope and direction of this project:

Key Questions:

Core Model Goals – Since we’re building this from scratch, what’s the primary focus?

• Are we aiming to replicate PVNet’s performance using only public data?
• Or are we looking to redesign the model to work better with open datasets?

Challenges with Open Data – What are the biggest obstacles when training a model only on open-source NWP data?

• Are there known gaps in data coverage, missing variables, or resolution issues?
• Have previous models struggled with this transition?

Baseline Comparison – Is there an existing baseline we should aim to match or improve upon?

• Would it be useful to run an initial benchmark with PVNet using only public data to see the current gap?

Model Architecture Decisions – Should we reuse parts of the PVNet model or start fresh?

• If certain components from PVNet can still be useful, which ones?
• Are there any recommended ML architectures (CNNs, Transformers, hybrid models) that have worked well for this task?

Training and Deployment – What’s the expected workflow?

• Can this be trained locally, or will we need cloud-based training?
• Will the final model be integrated back into PVNet, or will it function as a separate module?

First Steps – What’s the best way to get started?

• Would you recommend contributing to a small issue in PVNet first to understand the codebase?
• Or should I focus directly on defining the dataset pipeline and preprocessing steps?

[peterdudfield]

Hey @peterdudfield ,

I'm Yeswanth, and I'm really excited about contributing to the Open Data PVNet project as part of GSoC 2025. I have a strong background in Python and machine learning, and I'm eager to build the model from scratch using open Numerical Weather Prediction (NWP) data.

I've already started exploring the PVNet repo to understand the existing framework and get familiar with how things are structured. Before diving in, I wanted to clarify a few things about the scope and direction of this project:

Key Questions:

Core Model Goals – Since we’re building this from scratch, what’s the primary focus?

• Are we aiming to replicate PVNet’s performance using only public data? yes
• Or are we looking to redesign the model to work better with open datasets? Probably not at first,

Challenges with Open Data – What are the biggest obstacles when training a model only on open-source NWP data?

• Are there known gaps in data coverage, missing variables, or resolution issues? nope
• Have previous models struggled with this transition? we havent done it before.

Baseline Comparison – Is there an existing baseline we should aim to match or improve upon?

• Would it be useful to run an initial benchmark with PVNet using only public data to see the current gap? yes, always good to run a baseline

Model Architecture Decisions – Should we reuse parts of the PVNet model or start fresh?

• If certain components from PVNet can still be useful, which ones? I will try to use PVNet as it is, atleast at the start
• Are there any recommended ML architectures (CNNs, Transformers, hybrid models) that have worked well for this task? That could be afterwards

Training and Deployment – What’s the expected workflow?

• Can this be trained locally, or will we need cloud-based training? Might need access to a GPU
• Will the final model be integrated back into PVNet, or will it function as a separate module? probably seperate, and the idea is to expand to different countries

First Steps – What’s the best way to get started?

• Would you recommend contributing to a small issue in PVNet first to understand the codebase? yes, and check out https://github.com/openclimatefix/open-data-pvnet
• Or should I focus directly on defining the dataset pipeline and preprocessing steps? see issues in https://github.com/openclimatefix/open-data-pvnet

[chaitra-samant]

Hi @peterdudfield @Sukh-P, apologies for the ping

I’m Chaitra Samant, a second-year Computer Engineering student from VJTI Mumbai, passionate about open-source and sustainable tech solutions. I’ve actively contributed to open-source projects, completed Hacktoberfest, and won an open-source contribution contest.

I’m highly interested in contributing to Open Data PVNet for GSoC. I have experience with PyTorch, Deep Learning, and Web Development, and I’m eager to work on training the solar forecasting model using free NWP data.

I’d love to understand the next steps for potential contributors—are there any prerequisite tasks or areas I should focus on to prepare effectively? Looking forward to your guidance!

[A7med7x7]

Hello, @peterdudfield @Sukh-P. Perhaps this is the project that matches me. I recently worked as a machine learning engineer intern at AirQowere they used local sensors to provide data, and then they decided to launch a research on the possibility of the atomespheric measurements and their correlation with the air quality (PM2.5 Predicition), I developed a model that scored 3rd on the benchmark while being suitable for production environment I was then selected to implement my ML model on their platform were I was introduced to the world of MLOPs and models deployment. Experiments, model optimization, and validation are my thing. The research with airqo helped me with the foundations of NWP. I would love to discuss my fit to this project and if there is still a need for more hands to work on it.

[Aidin1999]

Hi @peterdudfield , @Sukh-P
I am Aidin Miralmasi, a Master's graduate in Data Science with a specialization in machine learning and data engineering. I recently came across the Open Data PVNet project on Gsoc, and I am highly interested in contributing to the development of the solar forecasting model using open-source data.

Your project's goal to leverage 100% open data for forecasting solar generation at the national level, starting with the UK, aligns perfectly with my skills and interests. I have experience with Python and Transflow, which I believe would be beneficial for this project.

I have reviewed the project guidelines and have a few questions regarding the specifics of the data and the model's architecture. I would also appreciate some guidance on writing a cover letter tailored to this opportunity, as I am eager to make a significant contribution.

Could we schedule a time to discuss this further? I am looking forward to the possibility of working together and contributing to the success of the Open Data PVNet project.

Thank you for considering my interest.

[Dhruvil03]

Hi,

I am Dhruvilsinh Chauhan, a grad student at San Diego State University. I am working as a AI/ML Research Assistant at JSB AI Center. I have experience with Python, PyTorch and ML algorithms. I am excited to train a model on new NWP data, create and integrate the solar forecasting pipeline.

Looking forward to hear from you.

Thank you,
Dhruvilsinh Ajaysinh Chauhan
Email: [email protected]
GitHub: https://github.com/Dhruvil03

[umangSharmacs]

Hey @peterdudfield , @Sukh-P,
I wanted to share a broad plan that I have in mind (will add more specifics in the full application) and get some general feedback from you. In particular, I'm curious if you'd prefer to jump into experimenting with different architectures straight away rather than diving deeply into feature engineering first. Also, which features have you guys seen work best historically?

Here's a rough outline of my plan:

• Baseline Model: Build an initial baseline using only public data to get a clear benchmark of how much “improvement” we need to target.
• EDA & Feature Engineering: Conduct exploratory data analysis on the public datasets to better understand the data. Engineer features that capture the spatial and temporal aspects crucial for forecasting solar output.
• Architectural Experimentation: Test out different architectures, like CNNs to pick up on localized spatial patterns and Temporal Fusion Transformers (TFT) or similar models to handle sequential, time-series data effectively.

A few questions to help guide and refine my approach:

1. What’s the ideal lead time for predictions? Should we be aiming for a few hours ahead, or are longer windows (like 24–48 hours) more impactful for practical decision-making?
2. How solid is the current NWP dataset in practice? Have there been any studies on its accuracy? And if we later push to generalize the model for other regions, do you think it would be worthwhile to eventually try predicting the NWP features too?
3. Do you have any favorite articles, papers, or resources that are great for getting up to speed on solar forecasting? I’d love a crash course, especially one that covers how our kind of data can be used effectively despite its quirks.
4. Instead of focusing only on evaluation metrics, are there any baseline models or reference implementations from past projects that are considered the gold standard for solar forecasting? What benchmarks should we aim to surpass?

Looking forward to your thoughts and feedback.

[vinayakyadav2709]

Hello @peterdudfield and @Sukh-P,

I'm Vinayak Yadav, currently working at SAC, ISRO on precipitation nowcasting and lightning prediction. I implemented DGMR for satellite data to benchmark our diffusion-based nowcasting model, and I'm planning to publish and deploy these projects (hopefully making them public) so they can serve the entire Indian subcontinent!

For PVNet, I have a few questions and proposals:

1. Key Private Dataset Variables:
   Are there any specific variables or data components from the private dataset that played a crucial role in improving prediction accuracy? If so, we might be able to identify alternative datasets or parameters to match or enhance those results.

2. Expanding to Indian Data:
   Although the current focus is on the UK, I'm very interested in helping integrate Indian NWP data into the pipeline. I have extensive experience with ERA5, GFS, and the CDS API. Whenever you're ready to explore data integration beyond the UK—even after GSoC—I'd be excited to contribute.

3. Exploring Multi-Level ML Techniques:
   At ISRO, I've been working on multi-level ML techniques (though I can't reveal too much until we publish our paper). While we'll start with PVNet, these techniques have shown promising results in lightning prediction and are more energy-efficient compared to some neural network alternatives. I'd love to discuss how we might incorporate these ideas into the project.

4. Distributed Data Training:
   Given the vast amount of weather data, we could also explore methods to dynamically load data across machines and use DDP for training. This is something we generally do at ISRO, and it could significantly reduce training times while allowing us to experiment with a broader range of models and methods.

Also, Thank you so much for all the incredible work on the Open Climate Fix project. I’ve personally benefited from it, and it's inspiring to see its positive impact on our community and the many researchers who rely on your efforts. Lastly, I'm really looking forward to your feedback and the chance to collaborate further.

Best regards,
Vinayak Yadav

[peterdudfield]

1. The datasets are available and details are in https://github.com/openclimatefix/open-data-pvnet
2. Yes, of course
3. sounds great
4. sure

[CuTyche]

Hi everyone!

I’m Shravani Jaiswal, a Computer Science Engineering student with interests in Machine Learning and Renewable Energy. I’m excited about contributing to Open Data PVNet as part of Google Summer of Code 2025.

I have experience with Python and basic ML (PyTorch), and I’m eager to deepen my understanding of solar forecasting and NWP data. I’ve gone through the project documentation and GitHub issues, and I’m particularly interested in:

1. Training the ML model on open NWP data

2. Benchmarking the solar forecasting model

3. Improving data preprocessing & handling missing values

This would be my first time contributing in open source so I would need some guidance I’d love to contribute to this project and learn from the mentors and community here! Could you suggest any beginner-friendly issues or key areas where I can start contributing?

[AoufNihed]

Excited to contribute! Looking into how multi-agent systems can improve forecasting and grid optimization. Keen to collaborate!

[samratadityakpd]

Hello @peterdudfield and @Sukh-P,

I'm interested in contributing to this project for Google Summer of Code 2025. I am 4th year student from C. V. Raman University. I have machine learning experience in Python and PyTorch. I’ve also worked on projects like Inception-Xception hybrid model (TensorFlow ,working on its PyTorch version), lstm_gru_cnn_sru_ensmeble and now working on cnn_sru hybrid model (everything in pytorch).

I am every interested in PVNET model as how it is different from other model e.g. resnet, googlenet, etc.

I’d love to learn more about the expectations for the ML pipeline and how best to prepare for the application process. Looking forward to your guidance!

[abelsaj2005]

Hi @peterdudfield and @Sukh-P, I'm Abel Saj, a CS and Statistics double major at UNC Chapel Hill and I'm interested in working on this project! I have experience in ML and Python, and I’ve worked on projects involving generative AI, computer vision, neural networks, and other machine learning models. With my statistics and computer science background, I can analyze and model complex datasets effectively. I'm excited about contributing to an open-source solar forecasting pipeline and working with NWP data.

[Shalvigour]

Respected sir,
I hope you're doing well. I recently came across the Open Climate Fix project, and I genuinely appreciate its concept and impact. I was keen on contributing earlier, but due to health issues and university exams, I couldn’t start on time. However, I’m now fully available and eager to contribute. I understand that an early start is ideal, but I wanted to check—if I begin contributing now, would I still have a fair chance of getting selected for GSoC ? If yes, I’d really appreciate any guidance on how I can make a meaningful contribution within the remaining time.

[nihadhadjs]

Dear Peter, Sukh, and the Open Climate Fix team,

I am writing to express my enthusiastic interest in contributing to the Open Data PVNet project as part of Google Summer of Code 2025. As a Ph.D. student in Artificial Intelligence, I am deeply engaged in research that leverages spatio-temporal modeling and deep learning, which strongly aligns with the challenges and goals of solar forecasting using Numerical Weather Prediction (NWP) data.

My current doctoral research focuses on motion detection and energy tracking in video scenes using spatio-temporal filters inspired by human vision. This has given me hands-on experience building and optimizing deep learning architectures that extract temporal dependencies in complex, dynamic data expertise that I am eager to bring to the PVNet forecasting pipeline.

During my Master’s degree in Decision Support and Intelligent Systems, I worked extensively on deep learning projects involving Recurrent Neural Networks (RNNs), LSTM networks, and Multilayer Perceptrons (MLPs). I’ve applied these models to behavior-based identification systems and instance segmentation tasks using Mask R-CNN. My strong background in PyTorch, Python, and handling large-scale datasets positions me well to contribute to building a solar forecasting model that relies entirely on open data.

What excites me most about this project is the mission to democratize clean energy forecasting using transparent, reproducible methods. I’m particularly interested in working with open-source communities and using machine learning not just for innovation, but also for environmental and societal benefit.

I’d love the opportunity to work closely with your team to develop a robust and benchmarked solar forecasting model for the UK and eventually help extend it to other regions. I'm confident that my research mindset, technical background, and collaborative spirit will make me a strong contributor to this initiative.

Thank you for considering my application. I look forward to the possibility of discussing how I can contribute to Open Data PVNet and support your mission at Open Climate Fix.

Warm regards,
Hadj Sahraoui Nihad
📧 [[email protected]]
LinkedIn: https://www.linkedin.com/in/nihad-hs
GitHub: github.com/nihadhsa2000

[pt-kunal-mishra]

Hii @peterdudfield and the Open Climate Fix team
actually i also want to contribute to this awesome project i did some research regarding the similar solar forecasting model in my graduation time want to learn more through the project.

[manideep3069]

Thanks for the clear project description! Given that public NWP data might lack the granularity (spatial/temporal resolution, specific features) of the private sources used in Quartz Solar, what are the main anticipated challenges for the PVNet model in achieving good accuracy with only this open data? Is the initial focus purely on training the existing architecture, or does it potentially include exploring specific architectural adaptations or feature engineering strategies to compensate for the difference in input data richness?

[ferro-gianluca-29]

Hi @peterdudfield, @Sukh-P,

I am Gianluca Ferro. I graduated with a Master’s degree in Electronic Engineering and currently work as a research fellow in Artificial Intelligence at ciparlabs , focusing on smart grids and renewable energy communities. My Master’s thesis centered on time series forecasting, where I developed a framework of predictors—ranging from gradient boosting to statistical models and neural networks—that can forecast energy consumption and generation from renewable sources.

I find your Open Data PVNet project especially compelling because it aligns with my interest in applying ML to optimize energy production and consumption. I’m excited about the opportunity to train my models on purely open-data resources and help benchmark their accuracy against existing solutions.

Before I dive in, I just have one question:

Are there any specific challenges or limitations encountered when using publicly available NWP data for forecasting solar generation, such as gaps in spatiotemporal resolution or reliability issues?

I look forward to possibly contributing to this project and helping advance the open-source solar forecasting capabilities. Thank you for your time, and I’m eager to discuss how I might fit into your development roadmap.

Best regards,
Gianluca

[mayankjain2710]

Hi @peterdudfield and @Sukh-P ,

I’m Mayank Jain, a second-year B.Tech student majoring in Computer Science with a focus on Data Science. I’m really excited about the Open Data PVnet project—it aligns closely with my interest in renewable energy and machine learning.

During my academic journey, I explored solar forecasting models as part of my coursework and hackathon experiences. I’d love to contribute to this project and also learn more by collaborating with your amazing team. I’ve gone through the GitHub repo and documentation, and I’m eager to get started.

Looking forward to your guidance and feedback!

Best regards,
Mayank Jain

[Harsh-Dhingra]

Hi @peterdudfield and team,

I'm Harsh Dhingra, currently enrolled in Master's student in Data Science and Msc Finance Before this, I worked as an ML Engineer at Lighthouse Global, where I developed and deployed several end-to-end ML solutions — including document classification systems with DistilBERT, RAG pipelines using Azure OpenAI, and optimized inference using NVIDIA Triton.

I just discovered this project and would love to contribute! Given my experience with large-scale model deployment and ML optimization, I’m confident I can help with both the model training and performance benchmarking aspects of the open data solar forecasting pipeline.

Looking forward to learning more about the project and how I can get involved!

Best,
Harsh

[emlweb]

Google Summer of Code 2025 applications are now closed.

We are currently reviewing all applications. Contributors will be announced 8 May 2025. Thank you!

[debarghya18]

Hi @peterdudfield

I am probably late to the party. But hi. I am very interested in this project and I'm considering contributing to it in GSOC'26. So, can you give me the 101s about the project and where I should start

[peterdudfield]

Great you are interested in GSOC 26. The best way is to read information above, and check out - https://github.com/openclimatefix/open-data-pvnet

[debarghya18]

hi @peterdudfield

are you guys coming for GSOC'26? i'll start working on this one project then.

[VikranthMaster]

Hi @peterdudfield

Great to see many people working on this project, i feel like climate issues are the most critical issue at hand right now. I would love to contribute to this project and participate in GSoC'26, since I want to make a real impact from my work. Could u provide me with the starting resources about this project.

Thank you,
Vikranth

[peterdudfield]

Great you are interested in GSOC 26. The best way is to read information above, and check out - https://github.com/openclimatefix/open-data-pvnet

There's a separate thread for GSCO 26