In this project the aim is to build statistical models for forecasting CO2 emissions from electricity production. The CO2 emissions depend on the form of production, almost zero emissions from solar and wind, and much higher from coal. Today, the Transmission System Operator (TSO) for each country in Europe report the current production levels for different forms of production to the organization ENTSO-E
. This data is collected and the CO2 emission levels for each country is calculated and very nicely illustrated at the website electricitymap.tmrow.co
- an open source project, see it on GitHub
. Since the electricity from solar and wind is weather dependent (of course!), then the CO2 emission level is heavily dependent on the weather. On the website the current wind conditions are displayed by taking data from GFS
. The core of the project will be to link the GFS forecasts with the CO2 and production form time series by building statistical models. In this way forecasts of CO2 emission up to a couple of days ahead can be calculated, which can be a very useful input for operation of energy systems in a green and sustainable way.
Good Python or R programming experience (or similar language) and some advanced courses in statistical modeling, preferably at least 02417 Time Series Analysis.