Python script for predicting Austrian electricity Spot prices I used Aylien News API to gather news and articles relevant to EU energy market - Articles.csv. The Aylien_news_SDK.py script uses their python SDK(https://docs.aylien.com/newsapi/interactive-documentation) to scrape and convert articles to csv file. Inside dshistory2018.xls are hourly spot prices(and volume) from EXAA for 2018.
I focused on predicting the daily MWh volume for 1 day ahead, to see if news articles affect(correlate) the future trading movements.
Preprocess_data.py --> The script merges Articles.csv and dshistory2018.xls such that each article represents 1 instance, meaning that there might be more daily articles predicting same day ahead volume. The script also transforms(vectorizes) the text making it ready for the learning phase.
For learning and prediction I used sklearn's RandomForestRegressor and R2_score to calculate accuracy. As expected it performed very badly, since the dataset contains only about 260 articles and the daily volume is a big number to predict accurately. It would be best to start with classification models to only predict increase/decrease in volume relative to day before.
RESULTS:
Most important features, in this case words:
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