Multi-loads Forecasting Based on Pattern Cross correlation and Temporal Patch Association

doi:10.19678/j.issn.1000-3428.0070572

Abstract

Abstract: To address the challenges of high noise levels, strong volatility, and difficulties in extracting periodic information in the loads of integrated energy systems, a multivariate load forecasting method based on pattern cross-correlation and temporal patch association mechanisms is proposed. The method analyzes the cross-lag relationships between external influencing factors and multivariate loads using the cross-correlation function, determining the most relevant time lags for data reconstruction and embedding. Building on this, a pattern cross-correlation mechanism is introduced to abstract the data into patterns based on their variation trends, mitigating the effects of fluctuations and noise. This is followed by the identification and extraction of key moments and periodic information based on cross-correlation theory. A temporal patch association mechanism is designed to divide the sequence into multiple subsequences, using mutual information methods to analyze and filter subsequences, thereby enhancing the model's ability to capture local continuity information in sequences. Multiple ablation and comparison experiments were conducted on the Comprehensive Energy System dataset from Arizona State University, Tempe campus. The ablation experiment results show that the data reconstructed through cross-lag analysis effectively improved the model's prediction accuracy. The pattern cross-correlation mechanism and temporal patch association mechanism enhanced the model's ability to identify key moments and capture local information, respectively. The comparison experiment results indicate that the proposed method outperforms five mainstream prediction models in multiple evaluation metrics, demonstrating higher prediction accuracy.

摘要： 针对综合能源系统负荷噪声大、波动性强、周期信息提取困难问题，提出一种基于模式互相关和时序片段关联机制的多元负荷预测方法。通过互相关函数分析外界影响因素与多元负荷间的交叉滞后关系，确定最相关的时滞并据此重构和嵌入数据；在此基础上，提出模式互相关机制，根据数据的变化趋势将其抽象为模式数据，以减轻数据波动与噪声的影响，然后基于互相关理论识别和提取关键时刻及周期信息；同时，设计时序片段关联机制，将序列划分为多个子序列，基于互信息方法分析和筛选子序列，增强模型对序列局部连续性信息的捕获能力。在美国亚利桑那州立大学坦佩校区综合能源系统数据集上进行多项消融实验和对比实验。消融实验结果表明，交叉滞后分析重构的数据有效提升了模型的预测精度，模式互相关机制和时序片段关联机制分别增强了模型的关键时刻识别能力和局部信息捕捉能力。对比实验结果表明，所提方法在多项评估指标上优于5种主流预测模型，具有较高的预测精度。

LI Zheng, LI Zhixiao, QIN Jinlei, GUO Changzhen. Multi-loads Forecasting Based on Pattern Cross correlation and Temporal Patch Association[J]. Computer Engineering, doi: 10.19678/j.issn.1000-3428.0070572.

李整, 李志霄, 秦金磊, 郭长珍. 基于模式互相关和时序片段关联机制的多元负荷预测[J]. 计算机工程, doi: 10.19678/j.issn.1000-3428.0070572.

/ Recommend / Download Citations

URL: https://www.ecice06.com/EN/10.19678/j.issn.1000-3428.0070572

References

[1] 史佳琪，谭涛，郭经，等．基于深度结构多任务学习的园区型综合能源系统多元负荷预测[J]．电网技术，2018，42(03)：698-707． Shi Jiaqi，Tan Tao，Guo Jing，et al．Multi-task learning based on deep architecture for various types of load forecasting in regional energy system integration[J]．Power System Technology，2018， 42(03)：698-707(in Chinese)．
[2] 叶剑华,曹旌,杨理,等.基于变分模态分解和多模型融合的用户级综合能源系统超短期负荷预测[J].电网技术,2022,46(07):2610-2622． YE Jianhua1, CAO Jing, YANG Li, LUO Fengzhang. Ultra Short-term Load Forecasting of User Level Integrated Energy System Based on Variational Mode Decomposition and Multi-model Fusion[J]. Power System Technology,2022,46(07):2610-2622．
[3] 赵登福,庞文晨,张讲社,等.基于贝叶斯理论和在线学习支持向量机的短期负荷预测[J].中国电机工程学报,2005,(13):8-13. Zhao Dengfu, Pang Wenchen, Zhang Jiangshe, Wang Xifan. Based on Bayesian theory and online learning SVM for short-term load forecasting [J]. Proceedingsof the CSEE，2005,(13):8-13.
[4] 赵峰，孙波，张承慧．基于多变量相空间重构和卡尔曼滤波的冷热电联供系统负荷预测方法[J]．中国电机工程学报，2016，36(02)：399-406． Zhao Feng，Sun Bo，Zhang Chenghui．Cooling，heating and electrical load forecasting method for CCHP system based on multivariate phase space reconstruction and kalman filter[J]．Proceedings of the CSEE，2016，36(02)：399-406(in Chinese)．
[5] 邓带雨，李坚，张真源，等．基于 EEMD-GRU-MLR 的短期电力负荷预测[J]．电网技术，2020，44(02)： 593-602． Deng Daiyu ， Li Jian ， Zhang Zhenyuan ， et al ． Short-term electric load forecasting based on EEMD-GRU-MLR[J]．Power System Technology， 2020，44(02)：593-602(in Chinese)．
[6] Antonio B ， Pierluigi C ， Pasquale F D ， et al ． Multivariate quantile regression for short-term probabilistic load forecasting[J]．IEEE Transactions on Power Systems，2020，35(1)：628-638．
[7] 牛东晓,谷志红,邢棉,等.基于数据挖掘的 SVM 短期负荷预测方法研究 [J]. 中国电机工程学报,2006,(18):6-12.． Niu Dongxiao，Gu Zhihong，Xingmian，et al．Study on forecasting approach to short-term load of SVM based on data mining[J]．Proceedings of the CSEE， 2006(18)：6-12(in Chinese)．
[8] Jason R，Etienne S．A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system[J]．Energy，2023，269.
[9] 史佳琪，张建华．基于多模型融合 Stacking 集成学习方式的负荷预测方法[J]．中国电机工程学报， 2019，39(14)：4032-4041． Shi Jiaqi，Zhang Jianhua．Load forecasting based on multi-model by stacking ensemble learning[J]．Proceedings of the CSEE，2019，39(14)： 4032-4041(in Chinese)．
[10] 王炜，冯斌，黄刚，等．基于自注意力编码器和深度神经网络的短期净负荷预测[J]．中国电机工程学报，2023，43(23)：9072-9084． Wang Wei，Feng Bin，Huang Gang，et al．Short-term net load forecasting based on self-attention encoder and deep neural network[J]．Proceedings of the CSEE， 2023，43(23)：9072-9084(in Chinese)．
[11] 李丹，孙光帆，缪书唯，等．基于多维时序信息融合的短期电力负荷预测方法[J]．中国电机工程学报， 2023，43(S1)：94-106． Li Dan ， Sun Guangfan ， Miao Shuwei, et al． A short-term power load forecasting method based on multidimensional temporal information fusion[J]．Proceedings of the CSEE，2023，43(S1)： 94-106(in Chinese)．
[12] Nada M ，Hamid O ，Ismael J ．Short-term electric load forecasting using an EMD-BI-LSTM approach for smart grid energy management system[J] .Energy & Buildings，2023，288．
[13] Huixin Liu ， Xiaodong Shen ， Xisheng Tang ， et al．Day-ahead electricity price probabilistic forecasting based on SHAP feature selection and LSTNet quantile regression[J]．Energies，2023，16(13):
[14] 王琛，王颖，郑涛，等．基于 ResNet-LSTM 网络和注意力机制的综合能源系统多元负荷预测[J]．电工技术学报，2022，37(07)：1789-1799． Wang Chen，Wang Ying，Zheng Tao，et al．Multi-energy load forecasting in integrated energy system based on ResNet-LSTM network and attention mechanism[J]．Transactions of China Electrotechnical Society，2022，37(07)：1789-1799(in Chinese)．
[15] Changchun Cai ， Yuanjia Li ， Zhenghua Su ， et al．Short-term electrical load forecasting based on VMD and GRU-TCN hybrid network[J] ． Applied Sciences，2022，12(13)：6647-6647．
[16] Wenhao Chenb ， Guangjie Han ， Hongbo Zhu ， et al．Short-term load forecasting with an ensemble model using densely residual block and Bi-LSTM based on the attention mechanism[J]．Sustainability，2022， 14(24)：16433-16433．
[17] Qin Y ，Zhiying L ，Hong L ， et al．An improved feature-time Transformer encoder-Bi-LSTM for short-term forecasting of user-level integrated energy loads[J]．Energy & Buildings，2023，297．
[18] Haixu Wu，Jiehui Xu，Jianmin Wang，et al．Autoformer: decomposition Transformers with auto-correlation for long-term series forecasting[J]．Advances in neural information processing systems ， 2021 ， 34 ： 22419-22430．
[19] Senfeng Cen，Lim C.G．Multi-task Learning of the PatchTCN-TST model for short-term multi-load energy forecasting considering indoor environments in a smart building[J]．IEEE Access，2024, 12：19553-19568.
[20] 鲁斌，霍泽健，俞敏．基于 LSTNet-Skip 的综合能源系统多元负荷超短期预测[J]．中国电机工程学报， 2023，43(06)：2273-2283． Lu Bin ， Huo Zejian ， Yu Min．Multi load ultra short-term forecasting of integrated energy systembased on LSTNet-Skip[J]．Proceedings of the CSEE， 2023，43(06)：2273-2283(in Chinese)．
[21] Chuanhui Zuo，Jialong Wang，Mingping Liu，et al．An ensemble framework for short-term load forecasting based on TimesNet and TCN[J]．Energies，2023， 16(14)：5330:
[22] 石卓见，冉启武，徐福聪．基于聚合二次模态分解及 Informer 的短期负荷预测[J]．电网技术，2023， 1467． Shi Zhuojian，Ran Qiwu，Xu Fucong. Short-term load forecasting based on aggregated secondary decomposition and Informer[J] ． Power System Technology，2023，1467 (in Chinese)．
[23] Dongxiao Niu，Min Yu，Lijie Sun，et al．Short-term multi-energy load forecasting for integrated energy systems based on CNN-BiGRU optimized by attention mechanism[J]．Applied Energy，2022，313．
[24] Feifei He，Jianzhong Zhou，Zhong-kai Feng，et al．A hybrid short-term load forecasting model based on variational mode decomposition and long short-term memory networks considering relevant factors with Bayesian optimization algorithm[J]．Applied Energy， 2019，237：103-116．
[25] 李云松,张智晟.基于 GRU-TGTransformer 的综合能源系统多元负荷短期预测 [J].电力系统保护与控制,2023,51(15):33-41. LI Yunsong, ZHANG Zhisheng. Multi load short-term forecasting of an integrated energy system based on a GRU TGTransformer[J]. Power System Protection and Control, 2023,51(15):33-41.
[26] 李鹏,罗湘淳,孟庆伟,等.基于 Spearman 相关性阈值寻优和 VMD-LSTM 的用户级综合能源系统超短期负荷预测[J].全球能源互联网,2024,7(04):406-420. LI Peng, LUO Xiangchun, MENG Qingwei, et al. Ultra short-term load forecasting of user-level integrated energy system based on Spearman threshold optimization, variational mode decomposition, and long short-term memory [J]. Journal of Global Energy Interconnection, 2024, 7(04): 406-420.
[27] 张大海,杨宇辰,刘艳梅,等.基于 EMD 与 Spearman 相关系数的混合直流线路纵联保护方法[J].电力系统保护与控制,2021,49(09):1-11. ZHANG Dahai, YANG Yuchen, LIU Yanmei, et al. Hybrid HVDC line pilot protection method based on EMD and Spearman correlation coefficient[J]. Power System Protection and Control, 2021, 49(9): 1-11.
[28] 黄灿,桂卫华,谢永芳,等.基于改进互相关函数的氧化铝碳分过程多重时滞辨识 [J]. 中国有色金属学报,2011,21(05):1186-1191. HUANG Can, GUI Weihua, XIE Yongfang, et al. Multi-delays identification for alumina carbonation decomposition process based on improved cross correlation function[J]. The Chinese Journal of Nonferrous Metals, 2011, 21(05): 1186-1191.
[29] 管奔,臧勇,吴迪平.基于互相关函数的轧机振动传递时间滞后分析 [C]// 第七届中国钢铁年会论文集,2009:443-448. Guan Ben, Zang Yong, Wu Diping. Study of Delay Time of Transfer of Rolling Mill Vibration Based on Cross-correlation Function.[C]// Proceedings of the 7th China Steel Conference, 2009:443-448.

Please choose a citation manager

Content to export