Natural Gas Load Forecasting Based on FCGA-LSTM and Transfer Learning

doi:10.3969/j.issn.1006-2475.2023.07.002

Abstract

Abstract: Abstract：High precision natural gas load forecasting is of great significance to the smooth and efficient operation of natural gas pipeline network. Most of the existing natural gas load forecasting methods are based on the condition of sufficient historical data， and there is little research on the problem of natural gas load forecasting in areas lacking historical data. To solve these problems， a short-term natural gas load forecasting method based on long and short-term memory（LSTM）neural network optimized by Fuzzy Coded Genetic Algorithm (FCGA) and transfer learning is proposed. First， the source domain and the target domain are selected， and the FCGA-LSTM prediction model is constructed by using a large amount of historical load data in the source domain. After model training and testing， the source domain model is moved to the target domain lacking data as a whole， and then a small amount of data in the target domain is used to fine tune and retrain the model. Finally， the target domain load prediction model is obtained. Taking a new residential area in Xi’an as an example， the results show that the prediction accuracy of the prediction method based on FCGA-LSTM and transfer learning is improved by 15.6 percentage points and 35.2 percentage points respectively compared with the combination method of LSTM and transfer learning， LSTM under non transfer learning， which proves the effectiveness of the model. The proposed method has certain guiding significance for the prediction of natural gas load in new urban areas lacking historical data.

Key words: natural gas, load forecasting, genetic algorithm, LSTM, transfer learning

CLC Number:

TP181

ZHANG Zhi-xia, XIE Bao-qiang. Natural Gas Load Forecasting Based on FCGA-LSTM and Transfer Learning[J]. Computer and Modernization, 2023, 0(07): 7-12.

References

［1］ YIN H， LI Z Z， ZUO J K， et al. Wasserstein generative adversarial network and convolutional neural network （WG-CNN） for bearing fault diagnosis［J］. Mathematical Problems in Engineering， 2020（6）：1-16.
［2］ JANSSENS O， SLAVKOVIKJ V， VERVISCH B， et al. Convolutional neural network based fault detection for rotating machinery［J］. Journal of Sound and Vibration， 2016，377：331-345.
［3］ WEN L， LI X Y， GAO L， et al. A new convolutional neural network-based data-driven fault diagnosis method［J］. IEEE Transactions on Industrial Electronics， 2018，65（7）：5990-5998.
［4］ PAN J， ZI Y Y， CHEN J L， et al. LiftingNet： A novel deep learning network with layerwise feature learning from noisy mechanical data for fault classification［J］. IEEE Transactions on Industrial Electronics， 2018，65（6）：4973-4982.
［5］ CHEN Z Y， LI W H. Multisensor feature fusion for bearing fault diagnosis using sparse autoencoder and deep belief network［J］. IEEE Transactions on Instrumentation and Measurement， 2017，66（7）：1693-1702.
［6］ GANAPATHY S， HAN K， THOMAS S， et al. Robust language identification using convolutional neural network features［C］// Proceedings of the Annual Conference of the International Speech Communication Association. 2014：1846-1850.
［7］ LI S， BYANG W L， ZHANG A S， et al. A novel method of bearing fault diagnosis in time-frequency graphs using inceptionresnet and deformable convolution networks［J］. IEEE Access， 2020，8：92743-92753.
［8］ GOODFELLOW I J， POUGET-ABADIE J， MIRZA M， et al. Generative adversarial nets［C］// Proceedings of the 27th International Conference on Neural Information Processing Systems. 2014：2672-2680.
［9］孙晓，丁小龙. 基于生成对抗网络的人脸表情数据增强方法［J］. 计算机工程与应用， 2020，56（4）：115-121.
［10］ ARJOVSKY M， CHINTALA S， BOTTOU L. Wasserstein GAN［J］. arXiv preprint arXiv：1701.07875v3， 2017.
［11］ WEI X， GONG B， LIU Z X， et al. Improving the improved training of wasserstein GANs： A consistency term and its dual effect［J］. arXiv preprint arXiv：1803.01541， 2018.
［12］蒋鹏飞，魏松杰. 基于深度森林与CWGAN-GP的移动应用网络行为分类与评估［J］. 计算机科学， 2020，47（1）：287-292.
［13］ LECUN Y， BOTTOU L， BENGIO Y，etal. Gradient-based learning applied to document recognition［J］. Proceedings of the IEEE， 1998，86（11）：2278-2324.
［14］沈涛，李舜酩，辛玉. 基于深度学习的旋转机械故障诊断研究综述［J］. 计算机测量与控制， 2020，28（9）：1-8.
［15］宋雨萌，谷峪，李芳芳，等. 人工智能赋能的查询处理与优化新技术研究综述［J］. 计算机科学与探索， 2020，14（7）：1081-1103.
［16］郝岩，白艳萍，张校非，等. 卷积神经网络在SAR目标识别中的应用［J］. 重庆理工大学学报（自然科学版）， 2018，32（5）：204-209.
［17］王丽苹，高瑞贞，张京军，等. 基于卷积神经网络的混凝土路面裂缝检测［J］. 计算机科学， 2019，46（S2）：584-589.
［18］ IOFFE S， SZEGEDY C. Batch normalization： Accelerating deep network training by reducing internal covariate shift［J］. arXiv preprint arXiv：1502.03167， 2015.
［19］ SRIVASTAVA N， HINTON G， KRIZHEVSKY A， et al. Dropout： A simple way to prevent neural networks from overfitting［J］. The Journal of Machine Learning Research， 2014，15（1）：1929-1958.
［20］ LOSHCHILOV I， HUTTER F. Fixing weight decay regularization in Adam［J］. arXiv preprint arXiv:1711.05101， 2017.
［21］ SMITH W A， RANDALL R B. Rolling element bearing diagnostics using the Case Western Reserve University data： A benchmark study［J］. Mechanical Systems and Signal Processing， 2015，64-65：100-131.
［22］李阳，张亚非，苗壮，等. 基于t-SNE卷积编码的图像检索方法［J］. 计算机应用研究， 2017，34（4）：1244-1248.
［23］ XU Q T， HUANG G， YUAN Y， et al. An empirical study on evaluation metrics of generative adversarial networks［J］. arXiv preprint arXiv：1806.07755， 2018.
［24］姚登举，杨静，詹晓娟. 基于随机森林的特征选择算法［J］. 吉林大学学报（工学版）， 2014，44（1）：137-141.
［25］吴军，王士同，赵鑫. 正负模糊规则系统、极限学习机与图像分类［J］. 中国图象图形学报， 2011，16（8）：1408-1417.
［26］张汝雷，王保民，苏欣平，等. 支持向量机（SVM）在机械故障诊断中的应用研究［J］. 军事交通学院学报， 2009，11（3）：39-42.
［27］ WANG J， GUO P， LI Y. DensePILAE： A feature reuse pseudoinverse learning algorithm for deep stacked autoencoder［J］. Complex & Intelligent Systems， 2021，8（8）. DOI：
10.1007/s40747-021-00516-5.
［28］ MAO W T， LIU Y M， DING L， et al. Imbalanced fault diagnosis of rolling bearing based on generative adversarial network： A comparative study［J］. IEEE Access， 2019，7：9515-9530.
［29］袁建虎，韩涛，唐建，等. 基于小波时频图和CNN的滚动轴承智能故障诊断方法［J］. 机械设计与研究， 2017，33（2）：93-97.

[1]	WANG Haiyang, GONG Tongxin, YANG Jintao, CHEN Zailong. Short-term Load Forecasting in Industrial Parks with Multi-scale Time Coding [J]. Computer and Modernization, 2024, 0(12): 59-65.
[2]	WU Xiuling1, ZHOU Sheng1, WANG Chunjuan1, YU Cuizhuo2, LIU Hao3. Research Progress in Ultra Short-term Power Load Forecasting Technology [J]. Computer and Modernization, 2024, 0(12): 108-115.
[3]	GONG Yicheng1, 2, LIU Qing1, 2. Beijing Opera Binary Classification Based on RF-LCE-BiLSTM-Attention-AMSSA Model [J]. Computer and Modernization, 2024, 0(11): 7-12.
[4]	LI Junchao1, YOU Fei1, ZHANG Chao2, SU Lele2, GONG Yan2 . BiLSTM-Attention Prediction Model and Error Analysis #br# Based on Novel Multi-objective Coati Optimization Algorithm [J]. Computer and Modernization, 2024, 0(11): 70-76.
[5]	WANG Jia1, GU Wenjun1, JU Weigang2, LI Yuwei1, ZHANG Yunlong2, MI Chuanmin3, ZHOU Zhipeng3. Environmental Topology Task Scheduling Based on Diverse Hierarchical Difference Optimization Genetic Algorithm [J]. Computer and Modernization, 2024, 0(10): 65-73.
[6]	LI Jin1, WEI Yanlong1, XUE Hongxin2, LIANG Haijian2. Influenza-like Illness Prediction Based on LSTM-SIR-EAKF [J]. Computer and Modernization, 2024, 0(09): 38-44.
[7]	ZHAO Dun1, SHE Xuebing2, WU Changxing3. Named Entity Recognition in Field of Party Building Based on BERT-BiLSTM-CRF [J]. Computer and Modernization, 2024, 0(09): 91-94.
[8]	MA Yong, WANG Jun, ZHANG Zijian, ZHAO Yuyang, ZHANG Jing, ZHOU Ming. Improved YOLOv8 Behavior Detection Algorithm for Intelligent Operation and#br# Maintenance System [J]. Computer and Modernization, 2024, 0(08): 43-48.
[9]	WANG Zhiqiang, ZHENG Shuang. Semi-supervised Image Generation Model Based on StyleGAN [J]. Computer and Modernization, 2024, 0(06): 14-18.
[10]	WANG Dongyue, LIU Hao. Multi-agent Genetic Algorithm Based Cloud Platform Anti-fake Data Injection Attack Method [J]. Computer and Modernization, 2024, 0(04): 21-26.
[11]	HU Mei-chen1, 2, LIU Dun-long1, 2, SANG Xue-jia1, 2, ZHANG Shao-jie3, CHEN Qiao4. Intelligent Identification Method of Debris Flow Scene Based on Camera Video Surveillance [J]. Computer and Modernization, 2024, 0(03): 41-46.
[12]	HU Chong-jia, LIU Jin-zhou, FANG Li. Unsupervised Domain Adaptation for Outdoor Point Cloud Semantic Segmentation [J]. Computer and Modernization, 2024, 0(01): 74-79.
[13]	ZHENG Li-rui, XIAO Xiao-xia, ZOU Bei-ji, LIU Bin, ZHOU Zhan. Named Entity Recognition in Electronic Medical Record Based on BERT [J]. Computer and Modernization, 2024, 0(01): 87-91.
[14]	FENG Xin-xin, BU Lei, ZHANG Xiao-yu, SHI Yu-feng. Analyzing to Shield Tunnel Segments Deformation Data Based on ICEEMDAN-LSTM [J]. Computer and Modernization, 2023, 0(11): 57-61.
[15]	LIU Xian-zhuo, DENG Wei-si, XIE En-yan. Adaptive Protection System for Distribution Network Considering Grid Connection of Distributed Generation [J]. Computer and Modernization, 2023, 0(09): 120-126.