Intraday Electricity Price Forecasting via LSTM and Trading Strategy for the Power Market: A Case Study of the West Denmark DK1 Grid Region

For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electrici...

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Bibliographic Details
Published in:Energies (Basel) 2024-06, Vol.17 (12), p.2909
Main Authors: Kılıç, Deniz Kenan, Nielsen, Peter, Thibbotuwawa, Amila
Format: Article
Language:English
Subjects:
Alternative energy sources
Case studies
Cost control
Data mining
Electricity
electricity price forecasting (EPF)
Energy consumption
Energy efficiency
Energy industry
Energy management systems
Energy resources
Energy trading
Financial markets
Forecasting
intraday electricity market
Kurtosis
long short-term memory (LSTM)
Machine learning
Market prices
Neural networks
Power
power market
Profits
Renewable resources
Securities trading
Solar energy
Supply & demand
Systems stability
time series
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Summary:For several stakeholders, including market players, customers, grid operators, policy-makers, investors, and energy efficiency initiatives, having a precise estimate of power pricing is crucial. It is easier for traders to plan, purchase, and sell power transactions with access to accurate electricity price forecasting (EPF). Although energy production and consumption topics are widely discussed in the literature, EPF and renewable energy trading studies receive less attention, especially for intraday market modeling and forecasting. Considering the rapid development of renewable energy sources, the article highlights the significance of integrating the deep learning model, long short-term memory (LSTM), with the proper trading strategy for short-term hourly renewable energy trading by utilizing two different spot markets. Day-ahead and intraday markets are taken into account for the West Denmark grid region (DK1). The time series analysis indicates that LSTM yields superior results compared to other benchmark machine learning algorithms. Using the predictions obtained by LSTM and the recommended trading strategy, promising profit values are achieved for the DK1 wind and solar energy use case, which ensures future motivation to develop a general and flexible model for global data.
ISSN:1996-1073
1996-1073
DOI:10.3390/en17122909