17RanRegression

Electricity price

I converted text columns to numbers, dropped rows with gaps and trained a random forest on ten wind, load and price columns to predict the half-hourly price.

Original tutorial by Aman Kharwal: Electricity Price Prediction (opens in a new tab)

What it printed in 2022

Numbers read from the saved notebook outputs. Cell numbers count every cell in the notebook, Markdown included, starting at 1.
Output	Value	Cell #
Predicted SMPEP2 price for the sample row	65.92	20

Cell # counts every cell from the top of the notebook, Markdown included, starting at 1.

The model was never scored. One of the inputs is the day-ahead price forecast (SMPEA), which is already close to the target.

The data

38,014 half-hourly records from the Irish electricity market, 2011 to 2013.

What I'd do differently

Split by date and compare against using the day-ahead price on its own.
Check feature importance to see whether the forest learnt anything beyond that one column.