Electricity is the commodity in the electricity market. In deregulated electricity market, generating companies (GENCOs) submit production bids one day ahead. When GENCOs decide the bids, both electricity load and price for the coming day are not known. So those decisions rely on the forecasting of electricity load and price. Electricity load forecasting has moved to an advanced stage both in the industry and academic with low enough prediction error, while electricity price forecasting is not as mature as electricity load forecasting in the respect of tools and algorithms. That is because the components of electricity price are more complicated than electricity load.
"Some machine learning projects succeed and some fail. What makes the difference? Easily the most important factor is the features used." -- Pedro Domingos in "A Few Things to Know about Machine Learning"
The mean absolute percentage error (MAPE) is used as the performance error. The MAPE is currently around 53%, which is high. The solution can be improved in the following respects.
- Create more efficient input variables, like electricity load. The electricity load data in New York ISO is provided in 5-minute interval. Those data has been retrieved, but is under manipulation process, like imputing missing value and aggregating to hour-level.
- Use other models like neural network.