Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives

被引:6
|
作者
Chai, Shanglei [1 ]
Li, Qiang [1 ]
Abedin, Mohammad Zoynul [2 ]
Lucey, Brian M. [3 ,4 ,5 ]
机构
[1] Shandong Normal Univ, Business Sch, Jinan 250014, Peoples R China
[2] Swansea Univ, Sch Management, Dept Accounting & Finance, Bay Campus,Fabian Way, Swansea SA1 8EN, Wales
[3] Trinity Coll Dublin, Trinity Business Sch, Dublin, Ireland
[4] Abu Dhabi Univ, Abu Dhabi, U Arab Emirates
[5] Jiangzi Univ, Ganzhou, Peoples R China
关键词
Determinants of electricity price; Dual decomposition method; Electricity price forecasting; Model optimization; Model structure; EXTREME LEARNING-MACHINE; NEURAL-NETWORK; HYBRID MODEL; SPOT PRICES; FEATURE-SELECTION; MARKET; DECOMPOSITION; POWER; OPTIMIZATION; VOLATILITY;
D O I
10.1016/j.ribaf.2023.102132
中图分类号
F8 [财政、金融];
学科分类号
0202 ;
摘要
Accurate electricity price forecasting (EPF) is crucial to participants and decision-makers within the electricity market. This paper reviews 62 screened literature works on EPF during 2012-2022 in terms of model structure and determinants of electricity price and discusses the evaluation process, model type, research sample, and prediction horizon. From the above efforts, we find that (1) data preprocessing and model optimization are often used to improve forecasting model accuracy; while performance evaluation is essential, extensive performance evaluation benchmarking is still missing; (2) considering electricity price determinants can significantly improve forecasting model accuracy, but there is disagreement over how many and which determinants should be accounted for; (3) while most existing research focuses on point forecasting, interval and density forecasting are more responsive to the range and uncertainty of electricity price changes.
引用
收藏
页数:26
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