基于D-Mamba模型的超短期火电机组发电负荷预测

doi:10.6040/j.issn.1672-3961.0.2024.194

摘要/Abstract

摘要： 随着新型电力系统逐渐完善,火电逐步从基荷电源向调峰电源转变。在新的发电背景下,火电机组面临着越来越严峻的运行考验和各类响应指标考核。超短期机组发电负荷预测需要考虑机组运行状态,以评估机组在未来超短期内的变负荷能力,精准的超短期机组发电负荷预测能够有效表征机组动态性能指标,有利于操作人员做出实时运行调整。针对投入自动发电量控制(auto generation control, AGC)运行方式的机组而言,AGC指令在发电负荷预测任务中起关键作用。因此,本研究在Mamba模型的基础上围绕AGC指令构建动态修正模块,实现对超短期机组负荷的精准预测。通过实际机组运行数据验证模型的预测精度。预测结果表明,本研究提出的预测模型能够实现更加精准的负荷预测。

关键词: 火电机组, 深度学习, 负荷预测, 状态空间模型, 自动发电量控制

Abstract: China progressively promoted the transition toward a new power system, with thermal power shifting from a base-load to a peak-load source. In this evolving power-generation context, thermal power units faced stricter challenges in unit tests and diverse response assessments. Ultra-short-term load forecasting for power units needed to account for the operating state of the unit to accurately assess its near-future load adjustment capability. Precise ultra-short-term load forecasting was critical for revealing a unit's dynamic performance indicators and assisting real-time operational adjustments. For units equipped with Auto-Generation Control(AGC), the AGC command served as a pivotal factor in forecasting power-generation loads. Hence, leveraging the Mamba model, this paper introduced a dynamic correction module centered on the AGC command, thereby ensuring precise ultra-short-term unit load forecasting. The model's performance was validated using actual operational data from a power unit, demonstrating its capability to achieve more accurate load forecasts.

Key words: thermal power unit, deep learning, load forecasting, state space models, auto generation control

中图分类号:

TM621.6

王新建,景志滨,孟凡成,石建国,张敏昊,张一帆,王庆华,朱彦恺. 基于D-Mamba模型的超短期火电机组发电负荷预测[J]. 山东大学学报 (工学版), 2026, 56(1): 169-178.

WANG Xinjian, JING Zhibin, MENG Fancheng, SHI Jianguo, ZHANG Minhao, ZHANG Yifan, WANG Qinghua, ZHU Yankai. Ultra-short-term thermal power unit load forecasting based on D-Mamba model[J]. Journal of Shandong University(Engineering Science), 2026, 56(1): 169-178.

参考文献

[1] 陈燚, 何山, 谢少华, 等. 基于合作博弈的风-光-电氢微网容量配置[J]. 太阳能学报, 2024, 45(2): 395-405. CHEN Yi, HE Shan, XIE Shaohua, et al. Capacity configuration of wind-photovoltaic-electric hydrogen microgrid based on cooperative game[J]. Acta Energiae Solaris Sinica, 2024, 45(2): 395-405.
[2] LV M L, ZHAO J P, CAO S X, et al. Prediction of temperature distribution in a furnace using the incremental deep extreme learning machine[J]. PeerJ Computer Science, 2023, 9: e1218.
[3] KHALID S, SONG J, RAOUF I, et al. Advances in fault detection and diagnosis for thermal power plants: a review of intelligent techniques[J]. Mathematics, 2023, 11(8): 1767.
[4] CUI J H, CHAI T Y, LIU X J. Deep-neural-network-based economic model predictive control for ultrasupercritical power plant[J]. IEEE Transactions on Industrial Informatics, 2020, 16(9): 5905-5913.
[5] WAN A P, CHANG Q, AL-BUKHAITI K, et al. Short-term power load forecasting for combined heat and power using CNN-LSTM enhanced by attention mechanism[J]. Energy, 2023, 282: 128274.
[6] 骆小满, 皇甫成, 阮江军, 等. 基于神经网络的热电联产机组热负荷和电负荷预测[J]. 热力发电, 2019, 48(9): 46-50. LUO Xiaoman, HUANGFU Cheng, RUAN Jiangjun, et al. Prediction of heat and electric load of cogeneration unit based on neural network[J]. Thermal Power Generation, 2019, 48(9): 46-50.
[7] 樊建升, 吴海滨, 刘泽军. 融合时间序列趋势的Dual-ESN机组负荷预测模型[J]. 电力系统及其自动化学报, 2023, 35(1): 152-158. FAN Jiansheng, WU Haibin, LIU Zejun. Dual-ESN prediction model for unit load fused with time series trend[J]. Proceedings of the CSU-EPSA, 2023, 35(1): 152-158.
[8] 徐聪, 胡永锋, 张爱平, 等. 基于特征筛选的综合能源系统多元负荷日前-日内预测[J]. 综合智慧能源, 2024, 46(3): 45-53. XU Cong, HU Yongfeng, ZHANG Aiping, et al. Multi-load day-ahead and intra-day forecasting for integrated energy systems based on feature screening[J]. Integrated Intelligent Energy, 2024, 46(3): 45-53.
[9] 刘璐瑶, 陈志刚, 沈欣炜, 等. 基于EMD-MLP组合模型的用电负荷日前预测[J]. 南方能源建设, 2024, 11(1): 143-156. LIU Luyao, CHEN Zhigang, SHEN Xinwei, et al. Day-ahead forecast of electrical load based on EMD-MLP combination model[J]. Southern Energy Construction, 2024, 11(1): 143-156.
[10] WEI N, YIN C, YIN L H, et al. Short-term load forecasting based on WM algorithm and transfer learning model[J]. Applied Energy, 2024, 353: 122087.
[11] WAZIRALI R, YAGHOUBI E, ABUJAZAR M S S, et al. State-of-the-art review on energy and load forecasting in microgrids using artificial neural networks, machine learning, and deep learning techniques[J]. Electric Power Systems Research, 2023, 225: 109792.
[12] FAN G F, HAN Y Y, LI J W, et al. A hybrid model for deep learning short-term power load forecasting based on feature extraction statistics techniques[J]. Expert Systems with Applications, 2024, 238: 122012.
[13] 王欣然. 基于相空间重构和双向长短期记忆网络的火电机组负荷预测建模研究[D]. 长沙: 长沙理工大学, 2022. WANG Xinran. Modeling of thermal power unit load prediction based on phase space reconstruction and bidirectional long short-term memory network[D]. Changsha: Changsha University of Science and Technology, 2022.
[14] 彭维珂, 聂椿明, 陈衡, 等. 基于智能算法的空冷火电机组负荷预测研究[J]. 华电技术, 2021, 43(3): 57-64. PENG Weike, NIE Chunming, CHEN Heng, et al. Study on load forecasting for air cooling thermal power units based on intelligent algorithm[J]. Huadian Technology, 2021, 43(3): 57-64.
[15] 张然然, 刘鑫屏. 火电机组超短期负荷预测[J]. 热力发电, 2018, 47(7): 52-57. ZHANG Ranran, LIU Xinping. Ultra-short-term load forecasting for thermal power units[J]. Thermal Power Generation, 2018, 47(7): 52-57.
[16] 郝晓光, 金飞, 张庆浩, 等. 基于mRMR与多级LSTM网络的火电机组响应AGC调控能力评估[J]. 热能动力工程, 2024, 39(5): 57-64. HAO Xiaoguang, JIN Fei, ZHANG Qinghao, et al. Assessment of AGC regulation capability of thermal power units based on mRMR and multi-level LSTM networks[J]. Journal of Engineering for Thermal Energy and Power, 2024, 39(5): 57-64.
[17] 颜子翼. 基于深度学习的火电机组短期负荷预测[D]. 青岛: 青岛大学, 2023. YAN Ziyi. Short-term load forecasting of thermal power units based on deep learning [D]. Qingdao: Qingdao University, 2023.
[18] 魏乐, 苏少忻, 房方, 等. 基于负荷预测的飞轮-火电系统自动发电控制响应性能优化[J]. 热力发电, 2023, 52(5): 92-99. WEI Le, SU Shaoxin, FANG Fang, et al. Optimization of automatic generation control response performance offlywheel-thermal power system based on load forecasting[J]. Thermal Power Generation, 2023, 52(5): 92-99.
[19] 弓林娟, 王文毓, 高耀岿, 等. 面向大规模新能源消纳的火电机组平行控制[J]. 动力工程学报, 2023, 43(2): 136-142. GONG Linjuan, WANG Wenyu, GAO Yaokui, et al. Parallel control of thermal power unit for large-scale renewable energy accommodation[J]. Journal of Chinese Society of Power Engineering, 2023, 43(2): 136-142.
[20] YAO J, CHEN J, XU J, et al. Nonlinear modeling and control of ultra-supercritical power plants with multi-level energy storage[J]. Applied Thermal Engineering, 2025: 128727.
[21] HONG F, JI W M, LIANG L, et al. A new assessment mechanism of primary frequency regulation capability for a supercritical thermal power plant in deep peaking[J]. Energy Science & Engineering, 2023, 11(2): 547-564.
[22] ZHANG H F, GAO M M, FAN H H, et al. A dynamic model for supercritical once-through circulating fluidized bed boiler-turbine units[J]. Energy, 2022, 241: 122914.
[23] Gu A, Dao T. Mamba: Linear-time sequence modeling with selective state spaces[C] //First conference on language Modeling, 2024.
[24] GU A, GOEL K, RÉ C. Efficiently modeling long sequences with structured state spaces[EB/OL].(2022-08-05)[2023-12-15]. https://doi.org/10.48550/arXiv.2111.00396
[25] 闫姝. 超超临界机组非线性控制模型研究[D]. 北京: 华北电力大学, 2013: 43-44. YAN Shu. A non-linear control model of ultra-supercritical unit[D]. Beijing: North China Electric Power University, 2013: 43-44.
[26] HE K M, ZHANG X Y, REN S Q, et al. Deep residual learning for image recognition[C] //2016 IEEE Conference on Computer Vision and Pattern Recognition(CVPR). Las Vegas, USA. IEEE, 2016: 770-778.
[27] LOSHCHILOV I, HUTTER F. Decoupled weight decay regularization[EB/OL].(2017-11-14)[2023-12-15]. https://arxiv.org/abs/1711.05101
[28] HOCHREITER S, SCHMIDHUBER J. Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780.
[29] CHO K, VAN MERRIENBOER B, GULCEHRE C, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation[J]. Computer Science, 2014.

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