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

被引:4
作者
Kilic, Deniz Kenan [1 ]
Nielsen, Peter [1 ]
Thibbotuwawa, Amila [2 ,3 ]
机构
[1] Aalborg Univ, Dept Mat & Prod, Fibigerstraede 16, DK-9220 Aalborg, Denmark
[2] Univ Moratuwa, Ctr Supply Chain Operat & Logist Optimizat, Katubedda 10400, Sri Lanka
[3] Univ Moratuwa, Dept Transport Management & Logist Engn, Katubedda 10400, Sri Lanka
关键词
long short-term memory (LSTM); electricity price forecasting (EPF); intraday electricity market; time series; energy trading; power market; data-driven prediction; machine learning; renewable energy;
D O I
10.3390/en17122909
中图分类号
TE [石油、天然气工业]; TK [能源与动力工程];
学科分类号
0807 ; 0820 ;
摘要
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.
引用
收藏
页数:15
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