Extreme Interval Electricity Price Forecasting of Wholesale Markets Integrating ELM and Fuzzy Inference

Abstract

The electricity wholesale market is inherently volatile in a deregulated market structure where market participants like power generators and retailors drive the price of electricity. Timely forecasting of the wholesale market prices by market participants has become of utmost importance in order to maximize on profits and minimize on risks. This report presents a hybrid method comprised of an extreme learning machine and a fuzzy inference engine to forecast price intervals using historical wholesale price extreme values (price maximum and minimum), historical load, generation and congestion hours, forecasted temperature and power outage data. This hybrid forecasting method has been tested on RTO Pennsylvania-New Jersey-Maryland (PJM) interconnection for the period July 1st, 2018 to February 8th, 2019, and is compared with individual extreme learning machine and the non-linear autoregressive neural network.