Open Energy Transition has started a project on synthetic electricity demand forecasts and I am reaching out because I believe that your help would be invaluable. The aim is to support energy planning models at an hourly, country-level scale—with the flexibility to drill down into regional forecasts.
A number of the important highlights in this project are:
Machine Learning for Demand Prediction: The project uses machine learning to create realistic electricity demand time series, based on historical electricity usage and weather data from countries with different climates and development levels.
Socio-Economic Factors: The model accounts for factors like EV and heat pump adoption, air conditioning use, industrial growth, urban expansion, and the impact of climate change on electricity consumption.
Future-Ready Forecasting: It will help energy planners predict demand in countries with limited historical data and model future scenarios that were previously impossible to forecast.
Collaboration for Smarter Planning: By contributing to a global electricity demand database, it will be helping energy planners make more informed decisions worldwide.
With better demand predictions, we expect the impact to ultimately be a smoother integration of renewable energy, improved grid stability, more efficient energy systems, and data-driven decisions for infrastructure and energy transitions.
With your help, we can get even further. If you know of any electricity demand time-series databases or are working on a similar project, please reach out. You can also respond to our LinkedIn post.
Thanks for reading.