考虑综合需求响应的Transformer-图神经网络综合能源系统多元负荷短期预测

doi:10.19595/j.cnki.1000-6753.tces.231267

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Abstract The accurate forecasting of multi load in an integrated energy system is imperative for ensuring the efficient and secure operation of diverse energy sources. Demand response technology not only enhances the equilibrium between energy supply and demand but also induces changes in users' general energy consumption habits, thereby amplifying the complexity and uncertainty of load forecasting. While existing research explores the coupling relationships among different energy sources in integrated energy systems and employs artificial intelligence methods for predictions, there is a noticeable gap in research concerning multi load forecasting that incorporates integrated demand response. To address these issues, this paper presents a Trans-GNN prediction model that incorporates integrated demand response considerations. Through the mathematical modeling of integrated demand response signals and their incorporation as input variables into the deep learning model, the accuracy of load predictions in demand response scenarios is improved.
Firstly, adhering to consumer psychology principles, we establish the power demand response center curve. By statistically calculating the fluctuation value of power demand response under various probability conditions, considering the correlation between response uncertainty and electricity price difference, we derive the power demand response signal with due consideration to uncertainty. Employing this signal and the coupling response principle, we ascertain the demand coupling response signal for cold and heat loads, culminating in the derivation of a integrated demand response signal. Then the Trans-GNN model integrates this signal, historical energy consumption data, and meteorological data for prediction. Through Transformer 's attention mechanism, the model realizes the probabilistic understanding of the integrated demand response signal, and dynamically extracts and filters the historical energy consumption data. Finally, the graph neural network is used to complete the further analysis of the input data and load forecasting.
In this paper, the data set of Tempe campus of Arizona State University in the United States is used as a simulation example to predict the multi load in a week, and the validity of the model is verified. The proposed model yields mean absolute percentage error (MAPE) values of 1.084%, 1.186%, and 1.477% for electric load, cooling load, and heat load, respectively. Corresponding root mean square error (RMSE) values are 387.100 kW, 703.008 kW, and 24.627 kW, while the weighted mean absolute percentage error (WMAPE) is 1.203%. Through the attention heat map, the way in which the model analyzes the input data is visualized, and the rationality of the model structure is verified. The error distribution of the model is statistically analyzed by the prediction error distribution, and the prediction stability of the model is further analyzed.
The following conclusions can be drawn from the simulation analysis: (1) The integrated demand response signal can effectively improve the short-term prediction accuracy of the model in the context of integrated demand response. (2) The reasonable application of attention mechanism can improve model 's ability to understand the importance of input information. By extracting historical data at critical time points, the model can grasp the regularity of load forecasting problems and effectively analyze the uncertainty of integrated demand response signals. (3) The application of graph neural network can make the model effectively analyze the coupling relationship between the operation mode of the integrated energy system and the multi load, and improve the prediction performance.

Key words： Integrated energy system integrated demand response coupling response graph neural network Transformer model multi load short-term forecasting

Received: 06 August 2023

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	Li Yunsong
	Zhang Zhisheng

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Li Yunsong,Zhang Zhisheng. Transformer Based Multi Load Short-Term Forecasting of Integrated Energy System Considering Integrated Demand Response[J]. Transactions of China Electrotechnical Society, 2024, 39(19): 6119-6128.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.231267 OR https://dgjsxb.ces-transaction.com/EN/Y2024/V39/I19/6119

[1] 李响, 牛赛. 双碳目标下源-网-荷多层评价体系研究[J]. 中国电机工程学报, 2021, 41(增刊1): 178-184.
Li Xiang, Niu Sai.Study on multi-layer evaluation system of source-grid-load under carbon-neutral goal[J]. Proceedings of the CSEE, 2021, 41(S1): 178-184.
[2] 刘英培, 黄寅峰. 考虑碳排权供求关系的多区域综合能源系统联合优化运行[J]. 电工技术学报, 2023, 38(13): 3459-3472.
Liu Yingpei, Huang Yinfeng.Joint optimal operation of multi-regional integrated energy system considering the supply and demand of carbon emission rights[J]. Transactions of China Electrotechnical Society, 2023, 38(13): 3459-3472.
[3] 余晓丹, 徐宪东, 陈硕翼, 等. 综合能源系统与能源互联网简述[J]. 电工技术学报, 2016, 31(1): 1-13.
Yu Xiaodan, Xu Xiandong, Chen Shuoyi, et al.A brief review to integrated energy system and energy Internet[J]. Transactions of China Electrotechnical Society, 2016, 31(1): 1-13.
[4] 代心芸, 陈皓勇, 肖东亮, 等. 电力市场环境下工业需求响应技术的应用与研究综述[J]. 电网技术, 2022, 46(11): 4169-4186.
Dai Xinyun, Chen Haoyong, Xiao Dongliang, et al.Review of applications and researches of industrial demand response technology under electricity market environment[J]. Power System Technology, 2022, 46(11): 4169-4186.
[5] 张沈习, 王丹阳, 程浩忠, 等. 双碳目标下低碳综合能源系统规划关键技术及挑战[J]. 电力系统自动化, 2022, 46(8): 189-207.
Zhang Shenxi, Wang Danyang, Cheng Haozhong, et al.Key technologies and challenges of low-carbon integrated energy system planning for carbon emission peak and carbon neutrality[J]. Automation of Electric Power Systems, 2022, 46(8): 189-207.
[6] 徐成司, 董树锋, 华一波, 等. 基于改进一致性算法的工业园区分布式综合需求响应[J]. 电工技术学报, 2022, 37(20): 5175-5187.
Xu Chengsi, Dong Shufeng, Hua Yibo, et al.Distributed integrated demand response of industrial park based on improved consensus algorithm[J]. Transactions of China Electrotechnical Society, 2022, 37(20): 5175-5187.
[7] 孙毅, 李飞, 胡亚杰, 等. 计及条件风险价值和综合需求响应的产消者能量共享激励策略[J]. 电工技术学报, 2023, 38(9): 2448-2463.
Sun Yi, Li Fei, Hu Yajie, et al.Energy sharing incentive strategy of prosumers considering conditional value at risk and integrated demand response[J]. Transactions of China Electrotechnical Society, 2023, 38(9): 2448-2463.
[8] 韩富佳, 王晓辉, 乔骥, 等. 基于人工智能技术的新型电力系统负荷预测研究综述[J]. 中国电机工程学报, 2023, 43(22): 8569-8592.
Han Fujia, Wang Xiaohui, Qiao Ji, et al.Review on artificial intelligence based load forecasting research for the new-type power system[J]. Proceedings of the CSEE, 2023, 43(22): 8569-8592.
[9] 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(7): 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(7): 1789-1799.
[10] 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(6): 2273-2283.
Lu Bin, Huo Zejian, Yu Min.Multi load ultra short-term forecasting of integrated energy system based on LSTNet-skip[J]. Proceedings of the CSEE, 2023, 43(6): 2273-2283.
[11] 张智晟, 于道林. 考虑需求响应综合影响因素的RBF-NN短期负荷预测模型[J]. 中国电机工程学报, 2018, 38(6): 1631-1638, 1899.
Zhang Zhisheng, Yu Daolin.RBF-NN based short-term load forecasting model considering comprehensive factors affecting demand response[J]. Proceedings of the CSEE, 2018, 38(6): 1631-1638, 1899.
[12] 王蓓蓓, 胥鹏, 王宣元, 等. 需求响应分布鲁棒建模及其大规模潜力推演方法[J]. 电力系统自动化, 2022, 46(3): 33-41.
Wang Beibei, Xu Peng, Wang Xuanyuan, et al.Distributionally robust modeling of demand response and its large-scale potential deduction method[J]. Automation of Electric Power Systems, 2022, 46(3): 33-41.
[13] 赵海彭, 苗世洪, 李超, 等. 考虑冷热电需求耦合响应特性的园区综合能源系统优化运行策略研究[J]. 中国电机工程学报, 2022, 42(2): 573-589.
Zhao Haipeng, Miao Shihong, Li Chao, et al.Research on optimal operation strategy for park-level integrated energy system considering cold-heat-electric demand coupling response characteristics[J]. Proceedings of the CSEE, 2022, 42(2): 573-589.
[14] 范添圆, 王海云, 王维庆, 等. 计及主/被动需求响应下基于合作博弈的微网-配电网协调优化调度[J]. 电网技术, 2022, 46(2): 453-463.
Fan Tianyuan, Wang Haiyun, Wang Weiqing, et al.Coordinated optimization scheduling of microgrid and distribution network based on cooperative game considering active/passive demand response[J]. Power System Technology, 2022, 46(2): 453-463.
[15] Vaswani A, Shazeer N, Parmar N, et al.Attention is all you need[C]//Proceedings of the 31st International Conference on Neural Information Processing Systems, Long Beach, California, USA, 2017: 6000-6010.
[16] Wu Zonghan, Pan Shirui, Long Guodong, et al.Connecting the dots: multivariate time series forecasting with graph neural networks[C]// Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, Virtual Event, CA, USA, 2020: 753-763.
[17] 林俐, 张玉. 激励型需求响应参与主动配电网优化调度的不确定性分析[J]. 华北电力大学学报(自然科学版), 2020, 47(5): 10-20.
Lin Li, Zhang Yu.Uncertainty analysis of incentive demand response participating in optimal scheduling of active distribution network[J]. Journal of North China Electric Power University (Natural Science Edition), 2020, 47(5): 10-20.
[18] Paterakis N G, Erdinç O, Catalão J P S. An overview of demand response: key-elements and international experience[J]. Renewable and Sustainable Energy Reviews, 2017, 69: 871-891.
[19] Arizona State University. AUS campus metabolism [DB/OL]. [2023-04-13]. http://cm.asu.edu/.
[20] National Renewable Energy Laboratory. National solar radiation database[DB/OL].[2023-04-13]. https://nsrdb.nrel.gov/.
[21] 郑睿程, 顾洁, 金之俭, 等. 数据驱动与预测误差驱动融合的短期负荷预测输入变量选择方法研究[J]. 中国电机工程学报, 2020, 40(2): 487-500.
Zheng Ruicheng, Gu Jie, Jin Zhijian, et al.Research on short-term load forecasting variable selection based on fusion of data driven method and forecast error driven method[J]. Proceedings of the CSEE, 2020, 40(2): 487-500.