• CSCD核心库收录期刊
  • 中文核心期刊
  • 中国科技核心期刊

电力建设 ›› 2018, Vol. 39 ›› Issue (10): 20-27.doi: 10.3969/j.issn.1000-7229.2018.10.003

• 现代人工智能在电力系统中的应用 栏目主持 文福拴教授、赵俊华教授、颜拥博士 • 上一篇    下一篇

采用长短期记忆深度学习模型的工业负荷短期预测方法

 

杨甲甲1, 刘国龙2, 赵俊华2, 文福拴3, 董朝阳1
  

  1. 1.新南威尔士大学电气与通信工程学院, 澳大利亚悉尼市 2052;2.香港中文大学(深圳)理工学院,广东省深圳市 518100;3.浙江大学电气工程学院,杭州市 310027
  • 出版日期:2018-10-01
  • 作者简介:杨甲甲 (1989), 男, 博士,副研究员, 主要从事电力经济与电力市场、智能电网、可再生能源接入等方面的研究工作; 刘国龙 (1995), 男, 博士研究生, 主要从事深度学习与大数据分析、智能电网、工业负荷建模等方面的研究工作; 赵俊华 (1980), 男, "青年千人计划"入选者,副教授,通信作者,主要从事电力系统分析与计算、智能电网、数据挖掘、人工智能、电力经济与电力市场等方面的研究工作; 文福拴 (1965), 男, 教授, 博士生导师, 主要从事电力系统故障诊断与系统恢复、电力经济与电力市场、智能电网与电动汽车等方面的研究工作; 董朝阳 (1971), 男, "千人计划"特聘专家, 讲座教授, 主要从事电力系统安全性、电力系统规划与管理、电力市场仿真与风险管理、数据挖掘等方面的研究工作。
  • 基金资助:
    国家自然科学基金重大研究计划培育项目(91746118);深圳市科技创新委员会国际合作研发项目(GJHZ20160301165723718)及基础研究项目(JCYJ20170410172224515)

A Long Short Term Memory Based Deep Learning Method for Industrial Load Forecasting

YANG Jiajia1, LIU Guolong2, ZHAO Junhua2, WEN Fushuan3, DONG Zhaoyang1   

  1. 1. School of Electrical Engineering and Telecommunications, University of New South Wales,Sydney 2052, Australia;2. School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen 518100, Guangdong Province, China; 3. College of Electrical Engineering, Zhejiang University, Hangzhou 310027, China
  • Online:2018-10-01
  • Supported by:
    This work is jointly supported by the Training Program of Major Research Plan of National Natural Science Foundation of China (No. 91746118), Shenzhen Municipal Science and Technology Innovation Committee International R&D Project (No. GJHZ20160301165723718) and Basic Research Project (No. JCYJ20170410172224515).

摘要: 工业负荷不同于其他电力负荷, 受气温、时间、人口等外部因素的影响较小, 其功率需求主要由相关企业的生产计划来决定。在电力市场环境下, 准确的负荷预测有助于工业用户更好地制定电力交易策略, 从而增加收益。在此背景下, 基于改进的长短期记忆(long short term memory, LSTM)深度学习网络模型, 提出了一种工业负荷短期预测算法。首先,在网络层次上构建层数更多即网络层次更深的LSTM深度学习负荷预测模型。接着, 在每个LSTM单元构成的隐含层中, 采用Dropout技术对神经元进行随机概率失活, 并通过正则化有效避免深度学习过拟合问题并改善了模型性能。然后, 采用真实的工业用户历史负荷数据对所提算法进行测试, 并与已有的短期负荷预测算法进行对比, 包括自回归滑动平均模型 (auto-regressive and moving average model, ARMA), 最邻近回归算法 (K nearest neighbor regression, KNN) 以及支持向量回归算法 (support vector regression, SVR)。仿真结果表明, 所提深度学习工业负荷短期预测算法相比于一些现有方法, 其预测准确度有明显提升,预测结果的平均绝对百分误差(mean absolute percentage error, MAPE)在9%以下。

关键词: 深度学习, 长短期记忆网络(LSTM), 工业负荷, 短期负荷预测

Abstract: Industrial load is mainly determined by the production schedule of a given factory, with a lower correlation to external factors such as temperature, time and demography. Under electricity market environment, accurate forecasting of industrial load is essential for industry end-users to develop profitable transaction plans. Given this background, this paper studied the short-term forecasting of industrial load and proposed a long short term memory (LSTM) network based deep learning algorithm for this purpose. Compared with some existing methods, the proposed LSTM-based deep learning algorithm is improved with respect to the following aspects. First, the proposed algorithm extended the layers of the deep learning network. Thus, both the capability of the network to extract information from historical data and the capability to forecast future load are strengthened. Secondly, the dropout technique is applied to each hidden layer composed of LSTM blocks, where a probability is assigned to a hidden unit in each layer of the network. The dropout technique can prevent the neural network from overfitting through the regularization. Consequently, the overall performance of the neural network is improved. Next, actual historical data of industrial load are used to test the proposed method. Case study results show that the proposed method can significantly improve the forecasting accuracy comparing with the auto-regressive and moving average model (ARMA), K nearest neighbor regression (KNN) and support vector regression (SVR) methods. Besides, the forecasting error measured by the mean absolute percentage error (MAPE) is less than 9% with the proposed forecasting method.

Key words: deep learning, long short term memory (LSTM) network, industrial load, short term load forecasting (STLF)

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