uk analysis dashboard

Internal dashboard for OCF to track UK forecast statistics.

The analysis dashboard is a tool that was developed for OCF’s internal use and continues to evolve.

Built with Streamlit, a Python-based framework made specifically for creating data apps, the dashboard tracks and displays Quartz Solar and other data model statistics, such as mean absolute error (MAE) on both the national and GSP level. The database provides the error statistic using Sheffield Solar's PVLive day-after updated values as the baseline --the larger the error, the less accurate the forecast.

Thanks to the analysis dashboard, OCF has a valuable feedback tool for understanding the accuracy of both the Quartz Solar forecast and other models in production.

Installation

In the main project folder, install requirements: pip install -r requirements.txt or pip3 install -r requirements.txt.

Run streamlit hello to check that Streamlit installed. A "Welcome to Streamlit!" page should open in the browser.

Create a login secret: echo "password = example" > src/.streamlit/secrets.toml.

Database connection

To run the app locally, you'll need to connect it to the forecast development database

OCF team members can connect to the forecast development database using these Notion instructions. Add DB_URL= (db_url from notion documents) to a secrets.toml file. Follow the instructions in the Notion document to connect to the database v.

Run app: cd src && streamlit run main.py.

files

main.py

main.py contains functions for the home page of the app, which focuses on MAE for the OCF Quartz Solar forecast.

forecast.py

forecast.py contains functions for the forecast page. The forecast page looks at how well each of OCF's forecast models is performing compared to PVLive updated truth values.

status.py

status.py contains functionality for the status pagwe and allows the OCF team to update the forecast status in the database. This is one of the advantages of using an interface like Streamlit, facilitating status updates in a database.

auth.py

auth.py contains code for the basic authenticaion that's been put in place.

pvsite_forecast.py

TODO

site_toolbox.py

TODO

plots/make_pinball_and_exceedance_plots.py

Function to make pinball and exceedance plots. This shows how good the probabilistic forecasts are doing.

plots/ramp_rate.py

Function to make ramp rate plots.

🛠️ infrastructure

.github/workflows contains some CI actions.

1. docker-pipeline.yml: Creates and publishes a docker image.

With any push to main, in order to deploy changes, the Terraform Cloud variable is updated with the commit reference and deployed to AWS Elastic Beanstalk.

Environmental Variables

• DB_URL: The database url which will be queried for forecasts
• password: The password for accessing the code
• SHOW_PVNET_GSP_SUM: Option to show pvnet_gsp_sum model or not. This defaults to zero

Contributors

The following folks have contributed to this repo.

Suleman Karigar 💻

Peter Dudfield 📆

devsjc 💻

rachel tipton 💻

braddf 💻

James Fulton 💻