A Review on Short-Term Electricity Price Forecasting Techniques for Energy Markets

被引:0
作者
Jiang, LianLian [1 ]
Hu, Guoqiang [2 ]
机构
[1] ASTAR, Inst Infocomm Res, Singapore 138632, Singapore
[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore
来源
2018 15TH INTERNATIONAL CONFERENCE ON CONTROL, AUTOMATION, ROBOTICS AND VISION (ICARCV) | 2018年
关键词
Electricity price forecasting (EPF); deep learning; artificial intelligence; energy market; time series models; EXTREME LEARNING-MACHINE; NEURAL-NETWORK MODEL; TIME-SERIES; WAVELET TRANSFORM; ARIMA MODELS; HYBRID ARIMA; LOAD; VECTOR; ALGORITHM; CLASSIFICATION;
D O I
暂无
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
The electricity price forecasting (EPF) is essential for decision-making mechanisms of market participants to survive in the deregulated and competing commercial environment. Due to special features of the electricity such as seasonality, the constant balance between production and consumption required by the system, and environmental dependencies, electricity prices generally shows extreme volatility and price spikes with the heteroscedasticity. This paper provides a survey of main EPF methodologies and the ultimate goal of this survey is to provide readers insights and guidelines for choosing different EPF techniques for day-ahead electricity markets. For each type of method, we briefly introduce its principle and then describe how it is applied in EPF. Many new EPF techniques developed recently are also discussed, especially the artificial intelligence forecasting methods. The pros and cons of each type of method are provided in a final table so that users can pay attention to when choosing them. In the final section, several promising methods and potential directions for further exploration are presented.
引用
收藏
页码:937 / 944
页数:8
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