Research on data-driven operation optimization of integrated energy systems

doi:10.3969/j.issn.2097-0706.2026.01.004

Abstract

Abstract:

In recent years， the rapid development of digital technologies such as the Internet of Things， big data， and artificial intelligence has provided new methods for the operation optimization of integrated energy systems（IES）. A data-driven operation optimization method for IES was proposed. For an industrial park with a self-contained energy station in northern China， a deep learning long short-term memory neural network model was used for multi-load joint forecasting and photovoltaic power forecasting， providing accurate support for the operation optimization of the energy station. The main energy supply equipment was modeled under full operating conditions through data-driven machine learning algorithms. Taking energy efficiency， economic， and comprehensive benefit indicators as optimization objectives， the particle swarm optimization algorithm was applied to obtain typical daily operation optimization results. Under the condition of optimal energy efficiency indicator， the comprehensive energy utilization rate of the system reached 83.0%， and the operating cost was 64 802 yuan. Under the condition of optimal economic indicator， the system operating cost was as low as 64 590 yuan， and the comprehensive energy utilization rate reached 79.3%. Under the condition of optimal comprehensive benefit indicator， compared to the actual operation of the energy station， the comprehensive energy utilization rate increased by 7.5%， and the operating cost was reduced by 6 444 yuan. The results indicate that this operation optimization method has practical significance for guiding the operation optimization of IES.

Key words: integrated energy system, multi-load joint forecasting, photovoltaic power forecasting, data-driven modeling, operation optimization

CLC Number:

TK01：TM732

XU Cong, XU Jingjing, JIANG Ting, XUE Dong, YAN Lichen. Research on data-driven operation optimization of integrated energy systems[J]. Integrated Intelligent Energy, 2026, 48(1): 34-42.

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References 26

[1]	谢小瑜. 可再生能源超短期发电功率预测的深度学习方法研究[D]. 广州: 华南理工大学, 2021.
	XIE Xiaoyu. Research on deep learning method for ultra-short-term power prediction of renewable energy[D]. Guangzhou: South China University of Technology, 2021.
[2]	CHENG W Y Y, LIU Y B, BOURGEOIS A J, et al. Short-term wind forecast of a data assimilation/weather forecasting system with wind turbine anemometer measurement assimilation[J]. Renewable Energy, 2017, 107: 340-351. doi: 10.1016/j.renene.2017.02.014
[3]	MA T, YANG H X, LU L. Solar photovoltaic system modeling and performance prediction[J]. Renewable & Sustainable Energy Reviews, 2014, 36: 304-315.
[4]	KAVASSERI R G, SEETHARAMAN K. Day-ahead wind speed forecasting using f-ARIMA models[J]. Renewable Energy, 2009, 34(5): 1388-1393. doi: 10.1016/j.renene.2008.09.006
[5]	王彩霞, 鲁宗相, 乔颖, 等. 基于非参数回归模型的短期风电功率预测[J]. 电力系统自动化, 2010, 34(16): 78-82, 91.
	WANG Caixia, LU Zongxiang, QIAO Ying, et al. Short-term wind power forecast based on non-parametric regression model[J]. Automation of Electric Power Systems, 2010, 34(16): 78-82, 91.
[6]	GUAN C, LUH P B, MICHEL L D, et al. Hybrid Kalman filters for very short-term load forecasting and prediction interval estimation[J]. IEEE Transactions on Power Systems, 2013, 28(4): 3806-3817. doi: 10.1109/TPWRS.2013.2264488
[7]	BAE K Y, JANG H S, SUNG D K. Hourly solar irradiance prediction based on support vector machine and its error analysis[J]. IEEE Transactions on Power Systems, 2017: 32(2): 935-945.
[8]	HERNÁNDEZ L, BALADRÓN C, AGUIAR J M, et al. Artificial neural network for short-term load forecasting in distribution systems[J]. Energies, 2014, 7(3): 1576-1598. doi: 10.3390/en7031576
[9]	张冬冬, 单琳珂, 刘天皓. 人工智能技术在风力与光伏发电数据挖掘及功率预测中的应用综述[J]. 综合智慧能源, 2025, 47(3): 32-46. doi: 10.3969/j.issn.2097-0706.2025.03.004
	ZHANG Dongdong, SHAN Linke, LIU Tianhao. Application of artificial intelligence technology in data mining and power prediction of wind and photovoltaic power generation[J]. Integrated Intelligent Energy, 2025, 47(3): 32-46.
[10]	牛哲文, 余泽远, 李波, 等. 基于深度门控循环单元神经网络的短期风功率预测模型[J]. 电力自动化设备, 2018, 38(5): 36-42.
	NIU Zhewen, YU Zeyuan, LI Bo, et al. Short-term wind power forecasting model based on deep gated recurrent unit neural network[J]. Electric Power Automation Equipment, 2018, 38(5): 36-42.
[11]	WEN L L, ZHOU K L, YANG S L, et al. Optimal load dispatch of community microgrid with deep learning based solar power and load forecasting[J]. Energy, 2019, 171: 1053-1065. doi: 10.1016/j.energy.2019.01.075
[12]	张倩, 马愿, 李国丽, 等. 频域分解和深度学习算法在短期负荷及光伏功率预测中的应用[J]. 中国电机工程学报, 2019, 39(8): 2221-2230.
	ZHANG Qian, MA Yuan, LI Guoli, et al. Application of frequency domain decomposition and deep learning algorithm in short-term load and photovoltaic power prediction[J]. Proceedings of the CSEE, 2019, 39(8): 2221-2230.
[13]	孙庆凯, 王小君, 张义志, 等. 基于LSTM和多任务学习的综合能源系统多元负荷预测[J]. 电力系统自动化, 2021, 45(5): 63-70.
	SUN Qingkai, WANG Xiaojun, ZHANG Yizhi, et al. Multiple load prediction of integrated energy system based on long short-term memory and multi-task learning[J]. Automation of Electric Power Systems, 2021, 45(5): 63-70.
[14]	WANG X, WANG S X, ZHAO Q Y, et al. A multi-energy load prediction model based on deep multi-task learning and ensemble approach for regional integrated energy systems[J]. International Journal of Electrical Power & Energy Systems, 2021, 126: 106583. doi: 10.1016/j.ijepes.2020.106583
[15]	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. doi: 10.3969/j.issn.2097-0706.2024.03.006
	XU Cong, HU Yongfeng, ZHANG Aiping, et al. Day-to-day forecasting of multi-load in comprehensive energy system based on feature screening[J]. Integrated Intelligent Energy, 2024, 46(3): 45-53. doi: 10.3969/j.issn.2097-0706.2024.03.006
[16]	WANG B, ZHANG L M, MA H R, et al. Parallel LSTM-based regional integrated energy system multienergy source-load information interactive energy prediction[J]. Complexity, 2019: 2019: 7414318.
[17]	LI K, MU Y C, YANG F, et al. Joint forecasting of source-load-price for integrated energy system based on multi-task learning and hybrid attention mechanism[J]. Applied Energy, 2024, 360: 122821. doi: 10.1016/j.apenergy.2024.122821
[18]	陈龙, 韩中洋, 赵珺, 等. 数据驱动的综合能源系统运行优化方法研究综述[J]. 控制与决策, 2021, 36(2): 283-294.
	CHEN Long, HAN Zhongyang, ZHAO Jun, et al. Summary of research on operation optimization methods of data-driven integrated energy system[J]. Control and Decision, 2021, 36(2): 283-294.
[19]	LIU J Y, SHI D L, LI G N, et al. Data-driven and association rule mining-based fault diagnosis and action mechanism analysis for building chillers[J]. Energy and Buildings, 2020, 216: 109957. doi: 10.1016/j.enbuild.2020.109957
[20]	YANG Y P, LI X E, YANG Z P, et al. The application of cyber physical system for thermal power plants: Data-driven modeling[J]. Energies, 2018, 11(4): 11040690.
[21]	LI X E, WANG N L, WANG L G, et al. A data-driven model for the air-cooling condenser of thermal power plants based on data reconciliation and support vector regression[J]. Applied Thermal Engineering, 2018, 129: 1496-1507. doi: 10.1016/j.applthermaleng.2017.10.103
[22]	杨挺, 赵黎媛, 王成山. 人工智能在电力系统及综合能源系统中的应用综述[J]. 电力系统自动化, 2019, 43(1): 2-14.
	YANG Ting, ZHAO Liyuan, WANG Chenshan. Review on application of artificial intelligence in power system and integrated energy system[J]. Automation of Electric Power Systems, 2019, 43(1): 2-14.
[23]	REYNOLDS J, AHMAD M W, REZGUI Y, et al. Operational supply and demand optimisation of a multi-vector district energy system using artificial neural networks and a genetic algorithm[J]. Applied Energy, 2019, 235: 699-713. doi: 10.1016/j.apenergy.2018.11.001
[24]	ANAND H, NARANG N, DHILLON J S. Multi-objective combined heat and power unit commitment using particle swarm optimization[J]. Energy, 2019, 172: 794-807. doi: 10.1016/j.energy.2019.01.155
[25]	李玉凯, 韩佳兵, 于春浩, 等. 基于随机森林和长短期记忆网络多元负荷预测的综合能源三层规划调度[J]. 现代电力, 2021, 38(6): 695-703.
	LI Yukai, HAN Jiabing, YU Chunhao, et al. Three-layer planning and dispatching of comprehensive energy based on multi-load forecasting of random forest and long-term and short-term memory network[J]. Modern Electric Power, 2021, 38(6): 695-703.
[26]	欧阳斌, 袁志昌, 陆超, 等. 考虑源-荷-储多能互补的冷-热-电综合能源系统优化运行研究[J]. 发电技术, 2020, 41(1): 19-29. doi: 10.12096/j.2096-4528.pgt.19100
	OUYANG Bin, YUAN Zhichang, LU Chao, et al. Study on optimal operation of cold-heat-electricity integrated energy system considering multi-energy complementarity of source-load-storage[J]. Power Generation Technology, 2020, 41(1): 19-29.

采集数据	单位
空调水瞬时热量	GJ/h
生活热水瞬时热量	GJ/h
园区总电负荷	kW
园区上/下网电量	kW·h
^#1/^#2内燃机燃气流量	m³/h
^#1/^#2内燃机发电功率	kW
^#1/^#2空调水制热量	kW
^#1/^#2直燃机空调水供/回水温度	℃
^#1/^#2直燃机空调水流量	t/h
^#1/^#2直燃机燃气流量	m³/h
^#2直燃机生活热水供/回水温度	℃
^#2直燃机生活热水流量	t/h
^#1/^#2生活热水板换进/出口温度	℃
^#1/^#2生活热水板换流量	t/h
^#1/^#2烟气热水板换进/出口温度	℃
^#1/^#2烟气热水板换流量	t/h

参数	设定值	参数	设定值
epochs	100	batch_size	8
num_layers	2	gamma	0.5
hidden_size	64	step_size	5
lr	0.1

参数	数值
epochs	100
num_layers	1
hidden_size	64
lr	0.01
batch_size	16

内燃机	MSE/kW²	RMSE/kW	MAE/kW	MAPE/%
^#1	517.287	22.744	18.12	2.961
^#2	420.895	20.516	16.18	2.696

直燃机	MSE/kW²	RMSE/kW	MAE/kW	MAPE/%
^#1	430.474	20.748	11.972	9.806
^#2	1 277.652	35.744	20.846	13.380