Comparison of Classical and Nonlinear Models for Short-Term Electricity Price Prediction
This work addresses electricity price forecasting for utilities and smart grid operators, but it is incremental as it tests existing methods without introducing new ones.
The paper compared regression decision trees, recurrent neural networks, and ARIMA for short-term electricity price prediction on a high-fluctuation ERCOT dataset, finding that regression decision trees achieved high performance relative to the other methods.
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.