基于特征空间变换与LSTM的中短期电煤价格预测

doi:10.12068/j.issn.1005-3026.2021.04.005

东北大学学报（自然科学版） ›› 2021, Vol. 42 ›› Issue (4): 483-493.DOI: 10.12068/j.issn.1005-3026.2021.04.005

基于特征空间变换与LSTM的中短期电煤价格预测

廖志伟，陈琳韬，黄杰栋，庄竞

(华南理工大学电力学院，广东广州510640)

修回日期:2020-08-13 接受日期:2020-08-13 发布日期:2021-04-15
通讯作者: 廖志伟
作者简介:廖志伟(1973-)，男，广西桂林人，华南理工大学副教授，博士.
基金资助:
国家自然科学基金资助项目(51437006).

Medium and Short-Term Electricity Coal Price Forecast Based on Feature Space Transformation and LSTM

LIAO Zhi-wei， CHEN Lin-tao， HUANG Jie-dong， ZHUANG Jing

School of Electric Power Engineering， South China University of Technology， Guangzhou 510640， China.

Revised:2020-08-13 Accepted:2020-08-13 Published:2021-04-15
Contact: LIAO Zhi-wei
About author:-
Supported by:
-

摘要/Abstract

摘要： 针对电煤价格影响因素多且非线性多时间滞后难以建模的问题，提出一种基于特征变换与LSTM的数据驱动的中短期电煤价格预测方法.为了充分挖掘海量数中蕴含的电煤价格规律，提出不同时间尺度颗粒度信息的特征变换方法;为解决多变量少样本造成过拟合，基于卡方分析和相关系数筛选中短期煤价的主要影响因素;以LSTM神经网络为基础，采用特征平移相关性分析方法确定不同影响特征序列的滞后性，通过主层次分析法优化模型中的信息冗余，在此基础上形成基于特征趋势的深度学习模型;利用多年历史数据及与多种模型的对比分析可知本文模型的有效性与准确性.

关键词: 电煤价格;连续预测;特征变换;长短神经网络;数据驱动；LSTM

Abstract: Aiming at the problem that there are many influencing factors of electricity coal prices and the nonlinearity and multiple time lags are difficult to model， a data-driven medium and short-term electricity coal price forecasting method based on feature transformation and LSTM was proposed. In order to fully excavate the electricity coal price law contained in the massive data， the feature transformation method of different time scale granularity information was proposed. In order to solve the problem of over-fitting caused by multiple variables and small samples， the main influencing factors of medium and short-term coal prices were screened based on chi-square analysis and correlation coefficient. Based on the LSTM neural network， the feature translation correlation analysis method was used to determine the hysteresis of different influencing feature sequences， and the information redundancy in the model was optimized by the main analytic method， and on this basis， a deep learning model based on feature trends was formed. The years of historical data and comparative analysis with various models show the effectiveness and accuracy of this model.

Key words: coal price; continuous forecasting; feature transformation; long short neural network; data driven; LSTM(long short-term memory)

中图分类号:

廖志伟，陈琳韬，黄杰栋，庄竞. 基于特征空间变换与LSTM的中短期电煤价格预测[J]. 东北大学学报（自然科学版）, 2021, 42(4): 483-493.

LIAO Zhi-wei， CHEN Lin-tao， HUANG Jie-dong， ZHUANG Jing. Medium and Short-Term Electricity Coal Price Forecast Based on Feature Space Transformation and LSTM[J]. Journal of Northeastern University(Natural Science), 2021, 42(4): 483-493.

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基于特征空间变换与LSTM的中短期电煤价格预测

Medium and Short-Term Electricity Coal Price Forecast Based on Feature Space Transformation and LSTM

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