Research Article
Day-Ahead Electricity Spike Price Forecasting Using a Hybrid Neural Network-Based Method
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@INPROCEEDINGS{10.1007/978-3-319-33681-7_36, author={Harmanjot Sandhu and Liping Fang and Ling Guan}, title={Day-Ahead Electricity Spike Price Forecasting Using a Hybrid Neural Network-Based Method}, proceedings={Smart City 360°. First EAI International Summit, Smart City 360°, Bratislava, Slovakia and Toronto, Canada, October 13-16, 2015. Revised Selected Papers}, proceedings_a={SMARTCITY360}, year={2016}, month={6}, keywords={Neural network Price spikes Day-ahead forecasting Electricity market}, doi={10.1007/978-3-319-33681-7_36} }- Harmanjot Sandhu
Liping Fang
Ling Guan
Year: 2016
Day-Ahead Electricity Spike Price Forecasting Using a Hybrid Neural Network-Based Method
SMARTCITY360
Springer
DOI: 10.1007/978-3-319-33681-7_36
Abstract
A hybrid neural network-based method is presented to predict day-ahead electricity spike prices in a deregulated electricity market. First, prediction of day-ahead electricity prices is carried out by a neural network along with pre-processing data mining techniques. Second, a classifier is used to separate the forecasted prices into normal and spike prices. Third, a second neural network is trained over spike hours with selected features and is used to forecast day-ahead spike prices. Forecasted spike and normal prices are combined to produce the complete day-ahead hourly electricity price forecasting. Numerical experiments demonstrate that the proposed method can significantly improve the forecasting accuracy.
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