Understanding how an API works, what data it contains, how the data is structured and formatted are all activities which have to take place upfront. One technique which helps with this is the process of collecting and transforming data into a state which is ready to be visualised. That’s what we’ll cover today 😎
For this tutorial we’re going to create a Python script which pulls Belgian solar forecast data via an API, post-processes it into a Pandas dataframe and finally visualises the output into a timeseries graph.
Why solar forecasts? Well solar forecasts are an interesting time-based dataset to work with since they’re critical for many different actors within the energy sector. For example grid operators, renewable asset owners/operators/developers, traders, smart buildings, energy management systems, the list goes on and on.
So let’s begin!
What you’re going to need
Querying the Elia solar forecast API using Postman
Before we get into any coding, let’s first take a look at the API which Elia (the Belgian Transmission System Operator) has created. On the technical operations page you can see that there’s only one operation/endpoint to integrate and a variety of parameters we can play with.