考虑温湿指数与耦合特征的综合能源负荷短期预测

doi:10.3969/j.issn.2097-0706.2023.07.008

综合智慧能源 ›› 2023, Vol. 45 ›› Issue (7): 70-77.doi: 10.3969/j.issn.2097-0706.2023.07.008

考虑温湿指数与耦合特征的综合能源负荷短期预测

金立¹(), 张力¹^,^*(), 唐杨¹, 唐侨¹, 任炬光¹, 杨焜², 刘小兵¹

1.西华大学流体及动力机械教育部重点实验室，成都 610039
2.中国石油工程建设有限公司西南分公司，成都 610041

收稿日期:2023-04-27 修回日期:2023-06-08 接受日期:2023-07-07 出版日期:2023-07-25 发布日期:2023-07-25
通讯作者: *张力（1982），男，副教授，硕士生导师，从事综合能源系统规划与运行优化等方面的研究，zhangl@mail.xhu.edu.cn。
作者简介:金立（1998），男，在读硕士研究生，从事综合能源负荷特性、负荷预测等方面的研究，jinli@stu.xhu.edu.cn。
基金资助:
国家重点研发计划项目(2018YFB0905200)

Short-term prediction on integrated energy loads considering temperature-humidity index and coupling characteristics

JIN Li¹(), ZHANG Li¹^,^*(), TANG Yang¹, TANG Qiao¹, REN Juguang¹, YANG Kun², LIU Xiaobing¹

1. Key Laboratory of Fluid and Power Machinery（Ministry of Education）， Xihua University， Chengdu 610039， China
2. China Petroleum Engineering & Construction Corporation Southwest Company， Chengdu 610041， China

Received:2023-04-27 Revised:2023-06-08 Accepted:2023-07-07 Online:2023-07-25 Published:2023-07-25
Supported by:
National Key R&D Program of China(2018YFB0905200)

摘要/Abstract

摘要：

针对综合能源负荷易受气象因素影响及其异质能量耦合特性所导致的预测建模复杂、准确性不高等问题，提出了一种考虑温湿指数与耦合特征的负荷短期预测模型。首先，在深入挖掘多元负荷耦合特征的基础上，结合温湿指数构造计及多因素影响的输入变量；然后，利用核主成分分析（KPCA）法在确保信息有效的前提下完成对预测输入空间的降维处理，并基于门控循环单元（GRU）神经网络进行预测建模，进一步引入Attention机制实现重要特征的差异化提取；最后，选取某实际系统电、冷负荷数据进行仿真。仿真结果表明，基于KPCA-GRU-Attention模型的电、冷负荷短期预测结果的均方根误差和平均绝对百分误差分别为1 025 kW，2.7%和2 167 kW，2.9%，准确性得到了显著提升。所提方法能够在考虑多因素影响的基础上有效提高综合能源负荷的短期预测精度，实现了对用能需求的精准感知。

关键词: 综合能源系统, 多元负荷, 温湿指数, 耦合特征, 核主成分分析, 门控循环单元神经网络, Attention机制, 短期负荷预测

Abstract:

To address the vulnerability of integrated energy load prediction to meteorological factors and the complexity and low accuracy of prediction models caused the coupling characteristics of heterogeneous energy， a short-term load forecasting model considering temperature-humidity index and coupling characteristics is proposed. Excavating the coupling characteristics of multiple loads， the input variables considering the influence of temperature-humidity index and multiple factors are constructed. Ensuring that the information is valid，kernel principal component analysis （KPCA） is used to complete the dimensional reduction of prediction input space， and the prediction model is built based on gated recurrent unit （GRU） neural network. Attention mechanism is introduced into the model to extract important differentiated features. Finally， the electric and cooling load data of a practical system are selected for simulation， and the results show that the root mean square errors and mean absolute percentage errors of the electric and cooling load predicted by KPCA-GRU-Attention model are 1 025 kW， 2.7% and 2 167 kW， 2.9 %， respectively. The accuracy has been significantly improved. The proposed model effectively improves the short-term prediction accuracy of integrated energy loads by considering the influence of multiple factors， realizing the accurate perception on energy demand.

Key words: integrated energy system, multiple load, temperature-humidity index, coupling characteristics, kernel principal component analysis, gated recurrent unit neural network, Attention mechanism, short-term load forecasting

中图分类号:

TK01：TM714

金立, 张力, 唐杨, 唐侨, 任炬光, 杨焜, 刘小兵. 考虑温湿指数与耦合特征的综合能源负荷短期预测[J]. 综合智慧能源, 2023, 45(7): 70-77.

JIN Li, ZHANG Li, TANG Yang, TANG Qiao, REN Juguang, YANG Kun, LIU Xiaobing. Short-term prediction on integrated energy loads considering temperature-humidity index and coupling characteristics[J]. Integrated Intelligent Energy, 2023, 45(7): 70-77.

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项目	参数
核函数	高斯核函数
优化器	Adam
学习率	0.001
隐含层层数	2
隐含层神经元数量	20
epoch	100
训练批次大小	32
学习率下降因子	0.9
交叉验证次数	10

模型	电负荷		冷负荷
模型	δ_RMSE/kW	δ_MAPE/%	δ_RMSE /kW	δ_MAPE/%
1	1 025	2.66	2 167	2.92
2	1 325	3.43	3 425	4.78
3	1 514	4.07	3 820	5.06
4	1 444	3.83	2 493	3.47
均值	1 327	3.50	2 976	4.06

考虑温湿指数与耦合特征的综合能源负荷短期预测

Short-term prediction on integrated energy loads considering temperature-humidity index and coupling characteristics

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