Paper Info
Title
Day-Ahead Price Forecasting in ERCOT Market Using Neural Network Approaches.
Abstract
Electricity is the "blood" of society. Electricity is a special commodity that is not storable, so its production and load should always be balanced to maintain a tightly regulated system frequency. Electricity production and load both depend on many factors, such as the weather, temperature, and wind. These characteristics make the dynamics of electricity price very different from that of any other commodities or financial assets. The electricity price can exhibit hourly, daily, and seasonal fluctuations, as well as abrupt unanticipated spikes. Now almost all electricity market participants use wind/load/price forecasting tools in their daily operations to optimize their operation plans, and bidding and hedging strategies, in order to maximize the profits and avoid price risks. However, the unreliable and inaccurate predictions with current forecasting tools have caused many serious problems, which can cause system instabilities and result in extreme prices even in the absence of scarcity. This paper presents an implementation of state of the art machine learning approaches into the forecasting tools for the ERCOT Day-Ahead market to improve the reliability and accuracy of electricity price prediction.
Year
DOI
Venue
2019
10.1145/3307772.3331024
E-ENERGY'19: PROCEEDINGS OF THE 10TH ACM INTERNATIONAL CONFERENCE ON FUTURE ENERGY SYSTEMS
Keywords
Field
DocType
Electricity Price Forecasting,Machine Learning,Neural Networks
Electricity market,Economics,Scarcity,Electricity,Commodity,Hedge (finance),Industrial organization,Bidding,Electricity price forecasting,Electricity generation
Conference
Citations 
PageRank 
References 
0
0.34
0
Authors
2
Name
Order
Citations
PageRank
Jian Xu122455.55
Ross Baldick232242.22