计算机科学
均方误差
平均绝对百分比误差
航程(航空)
能量(信号处理)
预测建模
数据集
计量经济学
平均绝对误差
能源市场
统计
数据挖掘
电
人工智能
数学
机器学习
材料科学
电气工程
复合材料
工程类
作者
Hongfang Lu,Xin Ma,Minda Ma,Senlin Zhu
标识
DOI:10.1016/j.cosrev.2020.100356
摘要
The accurate prediction of energy price is critical to the energy market orientation, and it can provide a reference for policymakers and market participants. In practice, energy prices are affected by external factors, and their accurate prediction is challenging. This paper provides a systematic decade review of data-driven models for energy price prediction. Energy prices include four types: natural gas, crude oil, electricity, and carbon. Through the screening, 171 publications are reviewed in detail from the aspects of the basic model, the data cleaning method, and optimizer. Publishing time, model structure, prediction accuracy, prediction horizon, and input variables for energy price prediction are discussed. The main contributions and findings of this paper are as follows: (1) basic prediction models for energy price, data cleaning methods, and optimizers are classified and described; (2) the structure of the prediction model is finely classified, and it is inferred that the hybrid model and prediction architecture with multiple techniques are the focus of research and the development direction in the future; (3) root mean square error, mean absolute percentage error, and mean absolute error are the three most frequently used error indicators, and the maximum mean absolute percentage error is less than 0.2; (4) the ranges of data size and data division ratio for energy price prediction in different horizons are given, the proportion of the test set is usually in the range of 0.05–0.35; (5) the input variables for energy price prediction are summarized; (6) the data cleaning method has a more significant role in improving the accuracy of energy price prediction than the optimizer.
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