  Electricity is bought and sold in wholesale markets at prices that fluctuate
significantly. Short-term forecasting of electricity prices is an important
endeavor because it helps electric utilities control risk and because it
influences competitive strategy for generators. As the "smart grid" grows,
short-term price forecasts are becoming an important input to bidding and
control algorithms for battery operators and demand response aggregators. While
the statistics and machine learning literature offers many proposed methods for
electricity price prediction, there is no consensus supporting a single best
approach. We test two contrasting machine learning approaches for predicting
electricity prices, regression decision trees and recurrent neural networks
(RNNs), and compare them to a more traditional ARIMA implementation. We conduct
the analysis on a challenging dataset of electricity prices from ERCOT, in
Texas, where price fluctuation is especially high. We find that regression
decision trees in particular achieves high performance compared to the other
methods, suggesting that regression trees should be more carefully considered
for electricity price forecasting.
