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.

准确预测能源价格对能源市场定位至关重要，它可以为决策者和市场参与者提供参考。实际上，能源价格受外部因素的影响，其准确预测具有挑战性。本文对能源价格预测的数据驱动模型进行了系统的十年回顾。能源价格包括四种类型：天然气，原油，电力和碳。通过筛选，从基本模型，数据清理方法和优化程序等方面对171种出版物进行了详细的审查。讨论了发布时间，模型结构，预测准确性，预测范围以及用于能源价格预测的输入变量。本文的主要贡献和发现如下：（1）能源价格的基本预测模型，数据清理方法，对优化器进行分类和描述；（2）对预测模型的结构进行了精细分类，推断混合模型和多种技术的预测体系结构是未来研究的重点和发展方向；（3）均方根误差，绝对绝对百分比误差和平均绝对误差是三个最常用的误差指标，最大平均绝对百分比误差小于0.2。（4）给出了不同水平下能源价格预测的数据大小和数据划分比例的范围，测试集的比例通常在0.05-0.35的范围内；（5）总结了能源价格预测的输入变量；（6）数据清理方法在提高能源价格预测的准确性方面比优化器具有更重要的作用。