Application and prospects of deep neural network in new energy systems

doi:10.3969/j.issn.2097-0706.2025.02.009

Abstract

Abstract:

Driven by the "dual carbon" goal， new energy sources such as wind and solar energy are developing rapidly. However， challenges such as wind and solar curtailment， resource waste， and low storage efficiency persist in energy production， consumption， and storage processes. Therefore， it is imperative to develop more intelligent new energy systems. Deep neural network（DNN）， a key technology in the development of next-generation artificial intelligence， has powerful capabilities in fitting complex functions due to its deep structure. It addresses the problem that traditional machine learning algorithms face when modeling and analyzing big data due to their limited ability to extract the most representative features from the data. The focus is on the application of DNN in new energy systems， providing an overview of DNN， the demand for DNN in new energy system， its applications in modeling and simulation， planning and optimization， operation and maintenance， operational control， and system management of new energy systems. The challenges of applying DNN in new energy systems are summarized and prospects for future development are outlined， aiming to provide reference for professionals in related industries.

Key words: "dual carbon" goal, new energy system, deep neural network, artificial intelligence, machine learning, big data analysis, large language model

CLC Number:

TK01：TP18

SHI Xin, LIU Qiyang, GAO Feng. Application and prospects of deep neural network in new energy systems[J]. Integrated Intelligent Energy, 2025, 47(2): 88-101.

Figures/Tables 11

Fig.1

Fig.2

Fig.3

Fig.4

Fig.5

Fig.6

Fig.7

Fig.8

Fig.9

Fig.10

Fig.11

References 82

[1]	温港成, 石鑫, 张怡, 等. 考虑设备变工况特性的园区综合能源系统两阶段规划优化方法研究[J]. 综合智慧能源, 2022, 44(10): 1-11. doi: 10.3969/j.issn.2097-0706.2022.10.001
	WEN Gangcheng, SHI Xin, ZHANG Yi, et al. Research on two-stage planning optimization approach for community integrated energy systems considering off-design conditions[J]. Integrated Intelligent Energy, 2022, 44(10): 1-11. doi: 10.3969/j.issn.2097-0706.2022.10.001
[2]	HINTON G E, SALAKHUTDINOV R R. Reducing the dimensionality of data with neural networks[J]. Science, 2006, 313(5786): 504-507. doi: 10.1126/science.1127647 pmid: 16873662
[3]	KRIZHEVSKY A, SUTSKEVER I, HINTON G E. ImageNet classification with deep convolutional neural networks[J]. Communications of the ACM, 2012, 60: 84-90.
[4]	GRAVES A, MOHAMED A R, HINTON G. Speech recognition with deep recurrent neural networks[C]// Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2013: 6645-6649.
[5]	GOODFELLOW I J, POUGET A J, MIRZA M, et al. Generative adversarial nets[C]// 2014 Neural Information Processing Systems. Montreal, Canada: NeurIPSF, 2014: 2672-2680.
[6]	HINTON G E, OSINDERO S, TEH Y W. A fast learning algorithm for deep belief nets[J]. Neural Computation, 2006, 18(7): 1527-1554. doi: 10.1162/neco.2006.18.7.1527 pmid: 16764513
[7]	GRAVES A, MOHAMED A R, HINTON G. Speech recognition with deep recurrent neural networks[C]// Proceedings of 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. IEEE, 2013: 6645-6649.
[8]	SUTSKEVER I, VINYALS O, LE Q V. Sequence to sequence learning with neural networks[J]. 2014(4): 3104-3112.
[9]	GRAVES A. Generating sequences with recurrent neural networks[J]. arXiv preprint arXiv, 2013:1308.0850.
[10]	GOODFELLOW I, BENGIO Y, COURVILLE A, et al. Deep learning[M]. Cambridge: MIT Press, 2016: 300-320.
[11]	朱永利, 石鑫, 王刘旺. 人工智能在电力系统中应用的近期研究热点介绍[J]. 发电技术, 2018, 39(3): 204-212. doi: 10.12096/j.2096-4528.pgt.2018.031
	ZHU Yongli, SHI Xin, WANG Liuwang. Recent research hotspot introduction on the application of artificial intelligence in power system[J]. Power Generation Technology, 2018, 39(3): 204-212. doi: 10.12096/j.2096-4528.pgt.2018.031
[12]	李红梅. 基于卷积神经网络的智能故障诊断方法研究[D]. 太原: 中北大学, 2021.
	LI Hongmei. Research on intelligent fault diagnosis method based on convolutional neural network[D]. Taiyuan: North University of China, 2021.
[13]	LECUN Y, BOTTOU L, BENGIO Y, et al. Gradient-based learning applied to document recognition[J]. Proceedings of the IEEE, 1998, 86(11): 2278-2324.
[14]	SIMONYAN K, ZISSERMAN A. Very deep convolutional networks for large-scale image recognition[EB/OL]. (2014-09-04)[2024-09-01]. https://arxiv.org/abs/1409.1556v6.
[15]	SZEGEDY C, LIU W, JIA Y Q, et al. Going deeper with convolutions[C]// Proceedings of 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2015: 1-9.
[16]	HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C]// Proceedings of 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2016: 770-778.
[17]	魏东, 龚庆武, 来文青, 等. 基于卷积神经网络的输电线路区内外故障判断及故障选相方法研究[J]. 中国电机工程学报, 2016, 36(S1): 21-28.
	WEI Dong, GONG Qingwu, LAI Wenqing, et al. Research on internal and external fault diagnosis and fault-selection of transmission line based on convolutional neural network[J]. Proceedings of the CSEE, 2016, 36(S1): 21-28.
[18]	胡越, 罗东阳, 花奎, 等. 关于深度学习的综述与讨论[J]. 智能系统学报, 2019, 14(1): 1-19.
	HU Yue, LUO Dongyang, HUA Kui, et al. Overview on deep learning[J]. CAAI Transactions on Intelligent Systems, 2019, 14(1): 1-19.
[19]	PETERS M, NEUMANN M, IYYER M, et al. Deep contextualized word representations[C]//Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics:Human Language Technologies. Association for Computational Linguistics, 2018: 2227-2237.
[20]	MNIH V, HEESS N, GRAVES A, KAVUKCUOGLU K. Recurrent models of visual attention[J]. Advances in Neural Information Processing Systems, 2014: 2204-2212.
[21]	BAHDANAU D, CHO K, BENGIO Y. Neural machine translation by jointly learning to align and translate[J]. arXiv preprint arXiv, 2014:1409.0473.
[22]	VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[J]. Advances in Neural Information Processing Systems, 2017: 5998-6008.
[23]	DEVLIN J, CHANG M W, LEE K, et al. BERT: Pre-training of deep bidirectional transformers for language understanding[EB/OL].(2018-10-11)[2024-09-01]. https://arxiv.org/abs/1810.04805v2.
[24]	RADFORD A, NARASIMHAN K, SALIMANS T, et al. Improving language understanding by generative pre-training[EB/OL]. (2020-03-01)[2024-09-01]. https://s3-us-west-2.amazonaws.com/openai-assets/research-covers/language-unsupervised/language_understanding_paper.pdf.
[25]	GUI J, SUN Z N, WEN Y G, et al. A review on generative adversarial networks: Algorithms, theory, and applications[J]. IEEE Transactions on Knowledge and Data Engineering, 2023, 35(4): 3313-3332.
[26]	CRESWELL A, WHITE T, DUMOULIN V, et al. Generative adversarial networks: An overview[J]. IEEE Signal Processing Magazine, 2018, 35(1): 53-65.
[27]	RADFORD A, METZ L, CHINTALA S, et al. Unsupervised representation learning with deep convolutional generative adversarial networks[EB/OL]. (2015-11-19)[2024-09-01]. https://arxiv.org/abs/1511.06434v2.
[28]	ZHU J Y, PARK T, ISOLA P, et al. Unpaired image-to-image translation using cycle-consistent adversarial networks[C]// Proceedings of 2017 IEEE International Conference on Computer Vision (ICCV). IEEE, 2017: 2242-2251.
[29]	MARTIN A, SOUMITH C, LÉON B. Wasserstein GAN[J]. arXiv preprint arXiv, 2017: 1701.07875.
[30]	GULRAJANI I, AHMED F, ARJOVSKY M, et al. Improved training of Wasserstein GANs[EB/OL]. (2017-03-31)[2024-09-01]. https://arxiv.org/abs/1704.00028v3.
[31]	MNIH V, KAVUKCUOGLU K, SILVER D, et al. Playing atari with deep reinforcement learning[J]. arXiv preprint arXiv, 2013: 1312.5602.
[32]	LI Y X. Deep reinforcement learning: An overview[EB/OL].(2017-01-25)[2024-09-01]. https://arxiv.org/abs/1701.07274.
[33]	TAN C Q, SUN F C, KONG T, et al. A survey on deep transfer learning[M]// Lecture Notes in Computer Science. Cham: Springer International Publishing, 2018: 270-279.
[34]	HOSSAIN LIPU M S, MIAH M S, HANNAN M A, et al. Artificial intelligence based hybrid forecasting approaches for wind power generation: progress, challenges and prospects[J]. IEEE Access, 2021, 9: 102460-102489.
[35]	SON J, PARK Y, LEE J N, et al. Sensorless PV power forecasting in grid-connected buildings through deep learning[J]. Sensors, 2018, 18(8): 2529.
[36]	魏梓轩, 韩晓娟, 李炫. 基于深度神经网络的梯次利用电池健康状态评估[J]. 太阳能学报, 2022, 43(5): 518-524. doi: 10.19912/j.0254-0096.tynxb.2022-0550
	WEI Zixuan, HAN Xiaojuan, LI Xuan. State of health assessment for echelon utilization batteries based on deep neural network[J]. Acta Energiae Solaris Sinica, 2022, 43(5): 518-524. doi: 10.19912/j.0254-0096.tynxb.2022-0550
[37]	GU B, ZHANG T R, MENG H, et al. Short-term forecasting and uncertainty analysis of wind power based on long short-term memory, cloud model and non-parametric kernel density estimation[J]. Renewable Energy, 2021, 164: 687-708.
[38]	YUAN X H, CHEN C, JIANG M, et al. Prediction interval of wind power using parameter optimized Beta distribution based LSTM model[J]. Applied Soft Computing, 2019, 82: 105550.
[39]	DUAN J. New energy power demand prediction and optimal scheduling based on artificial intelligence in smart grid[J]. International Journal of Low-Carbon Technologies, 2024, 1910. 1093: 1041-1048.
[40]	陈亭轩, 徐潇源, 严正, 等. 基于深度强化学习的光储充电站储能系统优化运行[J]. 电力自动化设备, 2021, 41(10): 90-98.
	CHEN Tingxuan, XU Xiaoyuan, YAN Zheng, et al. Optimal operation based on deep reinforcement learning for energy storage system in photovoltaic-storage charging station[J]. Electric Power Automation Equipment, 2021, 41(10): 90-98.
[41]	吕磊. 基于深度学习的储能装置容量预测模型及其优化控制研究[D]. 贵阳: 贵州大学, 2020.
	LYU Lei. Research on capacity prediction model and optimal control of energy storage device based on deep learning[D]. Guiyang: Guizhou University, 2020.
[42]	祁乐, 唐健, 陈新和, 等. 新能源实时错误数据辨识与修正深度学习模型[J]. 电网与清洁能源, 2020, 36(11): 19-28.
	QI Le, TANG Jian, CHEN Xinhe, et al. New energy real-time error data identification and correction deep learning model[J]. Power System and Clean Energy, 2020, 36(11): 19-28.
[43]	KONG Z Q, TANG B P, DENG L, et al. Condition monitoring of wind turbines based on spatio-temporal fusion of SCADA data by convolutional neural networks and gated recurrent units[J]. Renewable Energy, 2020, 146: 760-768.
[44]	姚晨. 基于深度学习的风机故障诊断与预警[D]. 北京: 华北电力大学, 2021.
	YAO Chen. Fault diagnosis and early warning of fan based on deep learning[D]. Beijing: North China Electric Power University, 2021.
[45]	YANG L X, ZHANG Z J. A conditional convolutional autoencoder-based method for monitoring wind turbine blade breakages[J]. IEEE Transactions on Industrial Informatics, 2021, 17(9): 6390-6398.
[46]	赵洪山, 刘辉海, 刘宏杨, 等. 基于堆叠自编码网络的风电机组发电机状态监测与故障诊断[J]. 电力系统自动化, 2018, 42(11): 102-108.
	ZHAO Hongshan, LIU Huihai, LIU Hongyang, et al. Condition monitoring and fault diagnosis of wind turbine generator based on stacked autoencoder network[J]. Automation of Electric Power Systems, 2018, 42(11): 102-108.
[47]	LYU Q C, LIU J Y, HE Y W, et al. Condition monitoring of wind turbines with implementation of interactive spatio-temporal deep learning networks[J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72: 3520811.
[48]	滕伟, 丁显, 史秉帅, 等. 基于WGAN-GP的风电机组传动链故障诊断[J]. 电力系统自动化, 2021, 45(22): 167-173.
	TENG Wei, DING Xian, SHI Bingshuai, et al. Fault diagnosis of wind turbine drivetrain based on Wasserstein generative adversarial network-gradient penalty[J]. Automation of Electric Power Systems, 2021, 45(22): 167-173.
[49]	蔡昌春, 何捷, 承敏钢, 等. 基于改进VMD-MCKD和深度残差网络的风机齿轮箱故障诊断[J]. 山东电力技术, 2024, 51(2): 67-78.
	CAI Changchun, HE Jie, CHENG Mingang, et al. Fault diagnosis of fan gearbox based on improved VMD-MCKD and deep residual network[J]. Shandong Electric Power, 2024, 51(2): 67-78.
[50]	卢锦玲, 张祥国, 张伟, 等. 基于改进辅助分类生成对抗网络的风机主轴承故障诊断[J]. 电力系统自动化, 2021, 45(7): 148-154.
	LU Jinling, ZHANG Xiangguo, ZHANG Wei, et al. Fault diagnosis of main bearing of wind turbine based on improved auxiliary classifier generative adversarial network[J]. Automation of Electric Power Systems, 2021, 45(7): 148-154.
[51]	戴森柏, 陈志聪, 吴丽君, 等. 利用LSTM和稳态时间序列的光伏阵列故障诊断方法[J]. 福州大学学报(自然科学版), 2022, 50(1): 54-60.
	DAI Senbai, CHEN Zhicong, WU Lijun, et al. A photovoltaic array fault diagnosis method using LSTM and steady-state time series[J]. Journal of Fuzhou University (Natural Science Edition), 2022, 50(1): 54-60.
[52]	LODHI E, WANG F Y, XIONG G, et al. A novel deep stack-based ensemble learning approach for fault detection and classification in photovoltaic arrays[J]. Remote Sensing, 2023, 15(5): 1277.
[53]	HONG F, SONG J, MENG H, et al. A novel framework on intelligent detection for module defects of PV plant combining the visible and infrared images[J]. Solar Energy, 2022, 236: 406-416.
[54]	AKRAM M W, LI G Q, JIN Y, et al. Automatic detection of photovoltaic module defects in infrared images with isolated and develop-model transfer deep learning[J]. Solar Energy, 2020, 198: 175-186.
[55]	KELLIL N, AISSAT A, MELLIT A. Fault diagnosis of photovoltaic modules using deep neural networks and infrared images under Algerian climatic conditions[J]. Energy, 2023, 263: 125902.
[56]	邓堡元, 何赟泽, 王洪金, 等. 光伏电池图像序列的深度学习检测方法[J]. 机械工程学报, 2021, 57(8): 98-106. doi: 10.3901/JME.2021.08.098
	DENG Baoyuan, HE Yunze, WANG Hongjin, et al. Deep learning inspection for photovoltaic cell image sequence[J]. Journal of Mechanical Engineering, 2021, 57(8): 98-106. doi: 10.3901/JME.2021.08.098
[57]	JEFFREY KUO C F, CHEN S H, HUANG C Y. Automatic detection, classification and localization of defects in large photovoltaic plants using unmanned aerial vehicles(UAV) based infrared (IR) and RGB imaging[J]. Energy Conversion and Management, 2023, 276: 116495.
[58]	王萍, 范凌峰, 程泽. 基于健康特征参数的锂离子电池SOH和RUL联合估计方法[J]. 中国电机工程学报, 2022, 42(4): 1523-1534.
	WANG Ping, FAN Lingfeng, CHENG Ze. A joint state of health and remaining useful life estimation approach for lithium-ion batteries based on health factor parameter[J]. Proceedings of the CSEE, 2022, 42(4): 1523-1534.
[59]	王英楷, 张红, 王星辉. 基于1DCNN-LSTM的锂离子电池SOH预测[J]. 储能科学与技术, 2022, 11(1): 240-245. doi: 10.19799/j.cnki.2095-4239.2021.0250
	WANG Yingkai, ZHANG Hong, WANG Xinghui. Hybrid 1DCNN-LSTM model for predicting lithium ion battery state of health[J]. Energy Storage Science and Technology, 2022, 11(1): 240-245. doi: 10.19799/j.cnki.2095-4239.2021.0250
[60]	WANG X C, HAO Z H, CHEN Z, et al. Joint prediction of Li-ion battery state of charge and state of health based on the DRSN-CW-LSTM model[J]. IEEE Access, 2023, 11: 70263-70273.
[61]	刘暾. 储能器件健康状态和寿命预测研究及应用[D]. 长沙: 中南大学, 2022.
	LIU Tun. Research and application of health state and life prediction of energy storage devices[D]. Changsha: Central South University, 2022.
[62]	BASTOS B Q, CYRINO OLIVEIRA F L, MILIDIÚ R L. U-Convolutional model for spatio-temporal wind speed forecasting[J]. International Journal of Forecasting, 2021, 37(2): 949-970.
[63]	HONG Y Y, SATRIANI T R A. Day-ahead spatiotemporal wind speed forecasting using robust design-based deep learning neural network[J]. Energy, 2020, 209: 118441.
[64]	CHEN Y R, DONG Z K, WANG Y, et al. Short-term wind speed predicting framework based on EEMD-GA-LSTM method under large scaled wind history[J]. Energy Conversion and Management, 2021, 227: 113559.
[65]	薛阳, 王琳, 王舒, 等. 一种结合CNN和GRU网络的超短期风电预测模型[J]. 可再生能源, 2019, 37(3): 456-462.
	XUE Yang, WANG Lin, WANG Shu, et al. An ultra-short-term wind power forecasting model combined with CNN and GRU networks[J]. Renewable Energy Resources, 2019, 37(3): 456-462.
[66]	骆钊, 吴谕侯, 朱家祥, 等. 基于多尺度时间序列块自编码Transformer神经网络模型的风电超短期功率预测[J]. 电网技术, 2023, 47(9): 3527-3537.
	LUO Zhao, WU Yuhou, ZHU Jiaxiang, et al. Wind power forecasting based on multi-scale time series block auto-encoder transformer neural network model[J]. Power System Technology, 2023, 47(9): 3527-3537.
[67]	WANG H Z, WANG G B, LI G Q, et al. Deep belief network based deterministic and probabilistic wind speed forecasting approach[J]. Applied Energy, 2016, 182: 80-93.
[68]	WANG K J, QI X X, LIU H D. A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network[J]. Applied Energy, 2019, 251: 113315.
[69]	张进, 刘运, 彭曙蓉. 基于特征挖掘的GRU-A光伏发电功率预测[J]. 实验室研究与探索, 2020, 39(5): 25-30.
	ZHANG Jin, LIU Yun, PENG Shurong. Photovoltaic power prediction based on feature mining and GRU-A[J]. Research and Exploration in Laboratory, 2020, 39(5): 25-30.
[70]	HOSSAIN M S, MAHMOOD H. Short-term photovoltaic power forecasting using an LSTM neural network and synthetic weather forecast[J]. IEEE Access, 2020, 8: 172524-172533.
[71]	RAFEEQ AHMED K, SAYEED F, LOGAVANI K, et al. Maximum power point tracking of PV grids using deep learning[J]. International Journal of Photoenergy, 2022, 2022: 1123251.
[72]	PHAN B C, LAI Y C, LIN C E. A deep reinforcement learning-based MPPT control for PV systems under partial shading condition[J]. Sensors, 2020, 20(11): 3039.
[73]	SONG Y D, CAO Q, DU X Q, et al. Control strategy based on wavelet transform and neural network for hybrid power system[J]. Journal of Applied Mathematics, 2013, 2013: 375840.
[74]	WANG S, TANG Y J, SHI J, et al. Design and advanced control strategies of a hybrid energy storage system for the grid integration of wind power generations[J]. IET Renewable Power Generation, 2015, 9(2): 89-98.
[75]	GARCÍA P, TORREGLOSA J P, FERNÁNDEZ L M, et al. Optimal energy management system for stand-alone wind turbine/photovoltaic/hydrogen/battery hybrid system with supervisory control based on fuzzy logic[J]. International Journal of Hydrogen Energy, 2013, 38(33): 14146-14158.
[76]	HASAN M N, TOMA R N, NAHID A A, et al. Electricity theft detection in smart grid systems: A CNN-LSTM based approach[J]. Energies, 2019, 12(17): 3310.
[77]	LI S, HAN Y H, YAO X, et al. Electricity theft detection in power grids with deep learning and random forests[J]. Journal of Electrical and Computer Engineering, 2019, 2019: 4136874.
[78]	ZHENG Z B, YANG Y T, NIU X D, et al. Wide and deep convolutional neural networks for electricity-theft detection to secure smart grids[J]. IEEE Transactions on Industrial Informatics, 2018, 14(4): 1606-1615.
[79]	ALRAWASHDEH K, PURDY C. Toward an online anomaly intrusion detection system based on deep learning[C]// Proceedings of 2016 15th IEEE International Conference on Machine Learning and Applications (ICMLA). IEEE, 2016: 195-200.
[80]	WANG L, ZHANG Z J, CHEN J Q. Short-term electricity price forecasting with stacked denoising autoencoders[J]. IEEE Transactions on Power Systems, 2017, 32(4): 2673-2681.
[81]	PENG L, LIU S, LIU R, et al. Effective long short-term memory with differential evolution algorithm for electricity price prediction[J]. Energy, 2018, 162: 1301-1314.
[82]	韩升科, 胡飞虎, 陈之腾, 等. 基于GCN-LSTM的日前市场边际电价预测[J]. 中国电机工程学报, 2022, 42(9): 3276-3286.
	HAN Shengke, HU Feihu, CHEN Zhiteng, et al. Day ahead market marginal price forecasting based on GCN-LSTM[J]. Proceedings of the CSEE, 2022, 42(9): 3276-3286.