Forecasting Price Interval of Wholesale Electricity Market Using a Hybrid Extreme Learning Machine and Fuzzy System

Date
2019
Authors
Bhagat, Manan Jatin
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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 of Extreme Learning Machine and Fuzzy Inference Systems forecasting model to forecast price intervals using historical wholesale price; historical load, generation and congestion hours, forecasted temperature and power outage data. This hybrid forecasting model has been tested on RTO Pennsylvania-New Jersey-Maryland interconnection for the period July 1st, 2018 to February 8th , 2019. This hybrid model is compared with Extreme Learning Machine and Non-Linear Autoregressive Neural Network methods. The Mean Absolute Percentage Error (MAPE), Root Mean Square Error(RMSE) and Correlation-coefficient are evaluated to analyze the performance of the model.

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This item is available only to currently enrolled UTSA students, faculty or staff.
Keywords
Electricity interval price forecasting, Extreme Learning Machine, Fuzzy Inference System, Mean absolute percentage error, Spike, Wholesale Electricity Markets
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Department
Electrical and Computer Engineering