综合能源系统中风电、光伏等可再生能源出力具有波动性和间歇性,精准的短期负荷预测有利于平抑可再生能源对系统运行的影响。系统中的多元负荷时间序列为典型的非平稳性信号,难以进行精准地预测。为了从数据层面提高综合能源系统短期负荷预测模型的精度,提出基于自适应局部迭代滤波（ALIF）的历史负荷数据分解方法,将历史负荷序列分解为具有不同频段模态函数的多个分量;针对预测模型训练中长时间序列处理困难及系统中多元负荷间耦合信息挖掘利用的问题,建立基于长短期记忆（LSTM）网络多任务学习的综合能源系统短期负荷预测模型。实验结果显示,与LSTM、ALIF-LSTM单任务学习、随机森林、LGBM方法相比,所提方法能够应对负荷波动剧烈的工况,预测精度较高,满足综合能源系统安全稳定运行控制的要求。
Abstract
The output of renewable sources such as wind power and photovoltaics in the integrated energy system is volatile and intermittent, and accurate short-term load forecasting is beneficial to smooth the impact of renewable energy on system operation. Short-term load prediction is the basis for the safe and stable operation of the integrated energy system, while the time series of multiple loads of the system is a typical non-stationary signal with strong randomness, which makes it difficult to make an accurate prediction. In order to improve the accuracy of the short-term load prediction model of the integrated energy system from the data level, a historical load data decomposition method based on ALIF is proposed, which decomposes the historical load sequence into multiple components with different frequency band modal functions. In order to solve the problem of long time series processing in predictive model training and coupling information mining among multiple loads in the system, a short-term load forecasting model for integrated energy system based on LSTM multi-task learning was proposed. Experimental results show that, compared with LSTM, ALIF-LSTM single-task learning, random forest, and LGBM, the proposed method can cope with the condition of severe load fluctuation, and has a higher prediction accuracy, which meets the requirements of safe and stable operation and control of the integrated energy system.
关键词
可再生能源 /
长短期记忆网络 /
多任务学习 /
自适应局部迭代滤波 /
负荷预测 /
综合能源系统
Key words
renewable energy /
long short-term memory /
multi-task learning /
ALIF /
load forecasting /
integrated energy systems
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参考文献
[1] 艾芊, 郝然. 多能互补、集成优化能源系统关键技术及挑战[J]. 电力系统自动化, 2018, 42(4): 1-10, 46.
AI Q, HAO R.Key technologies and challenges for multi-energy complementarity and optimization of integrated energy system[J]. Automation of electric power systems, 2018, 42(4): 2-10, 46.
[2] 李薇, 包哲, 杨涵晟, 等. 基于区间数理论的园区分布式综合能源系统效益及影响因素分析[J]. 太阳能学报, 2020, 41(2): 339-346.
LI W, BAO Z, YANG H S, et al.Cost-benefit and influencing factors analysis of distributed comprehensive energy system based on interval number theory[J]. Acta energiae solaris sinica, 2020, 41(2): 339-346.
[3] 谈金晶, 李扬. 多能源协同的交易模式研究综述[J].中国电机工程学报, 2019, 39(22): 6483-6496.
TAN J J, LI Y.Review on transaction mode in multi-energy collaborative market[J]. Proceedings of the CSEE, 2019, 39(22): 6483-6496.
[4] 王利猛, 王诗清, 石永富, 等. 计及储能装置平抑风光功率波动的微电网优化运行[J]. 太阳能学报, 2015, 36(1): 227-235.
WANG L M, WANG S Q, SHI Y F, et al.Optimal operation of micro-grid considering energy storage system smoothing wind turbine and photovoltaic power fluctuations[J]. Acta energiae solaris sinica, 2015, 36(1): 227-235.
[5] 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 T, ZHAO L Y, WANG C S.Review on application of artificial intelligence in power system and integrated energy system[J]. Automation of electric power systems, 2019, 43(1): 2-14.
[6] 陆继翔, 张琪培, 杨志宏, 等. 基于CNN-LSTM混合神经网络模型的短期负荷预测方法[J]. 电力系统自动化, 2019, 43(8): 131-137.
LU J X, ZHANG Q P, YANG Z H, et al.Short-term load forecasting method based on CNN-LSTM hybrid neural network model[J]. Automation of electric power systems, 2019, 43(8): 131-137.
[7] 赵兵, 王增平, 纪维佳, 等. 基于注意力机制的CNN-GRU短期电力负荷预测方法[J]. 电网技术, 2019, 43(12): 4370-4376.
ZHAO B, WANG Z P, JI W J, et al.A short-term power load forecasting method based on attention mechanism of CNN-GRU[J]. Power system technology, 2019, 43(12): 4370-4376.
[8] KHAN M, JAVAID N, IQBAL M N, et al.Load prediction based on multivariate time series forecasting for energy consumption and behavioral analytics[C]// Proceedings of the 12th International Conference on Complex, Intelligent, and Software Intensive Systems, Kunibiki Messe, Matsue, Japan, 2018.
[9] SAJJAD S, SHAMSHIRBAND S, ALIZAMIR M, et al.Extreme learning machine for prediction of heat load in district heating systems[J]. Energy and buildings, 2016, 122: 222-227.
[10] 赵峰, 孙波, 张承慧. 基于多变量相空间重构和卡尔曼滤波的冷热电联供系统负荷预测方法[J]. 中国电机工程学报, 2016, 36(2): 399-406, 596.
ZHAO F, SUN B, ZHANG C H.Cooling, heating and electrical load forecasting method for CCHP system based on multivariate phase space reconstruction and Kalman filter[J]. Proceedings of the CSEE, 2016, 36(2): 399-406, 596.
[11] CHAN J C, MA H, SAHA T K, et al.Self-adaptive partial discharge signal de-noising based on ensemble empirical mode decomposition and automatic morphological thresholding[J]. IEEE transactions on dielectrics and electrical insulation, 2014, 21(1): 294-303.
[12] 梁智, 孙国强, 李虎成, 等. 基于VMD与PSO优化深度信念网络的短期负荷预测[J]. 电网技术, 2018, 42(2): 598-606.
LIANG Z, SUN G Q, LI H C, et al.Short-term load forecasting based on VMD and PSO optimized deep belief network[J]. Power system technology, 2018, 42(2): 598-606.
[13] 杨再鹤, 向铁元, 郑丹. 基于小波变换和SVM算法的微电网短期负荷预测研究[J]. 现代电力, 2014, 31(3): 74-79.
YANG Z H, XIANG T Y, ZHENG D.Short-term load forecasting of microgrid based on wavelet transform and support vector machines[J]. Modern electric power, 2014, 31(3): 74-79.
[14] KARTHIK T, UMARIKAR A C, JAIN T.Empirical wavelet transform based single phase power quality indice[C]//18th National Power Systems Conference, Guwahati, India, 2014.
[15] BARTA G, NAGY G B G, GYULA B. GEFCOM 2014 -Probabilistic electricity price forecasting[M]. 2015.
[16] 张良均. Python数据分析与挖掘实战[M]. 北京: 机械工业出版社, 2015.
ZHANG L J.Python data analysis and mining practice[M]. Beijing: China Machine Press, 2015.
[17] CICONE A, LIU J F, ZHOU H M.Adaptive local iterative filtering for signal decomposition and instantaneous frequency analysis[J]. Applied and computational harmonic analysis, 2016, 41(2): 384-411.
[18] GOODFELLOW L, BENGIO Y, COURVILLE A.深度学习[M]. 北京: 人民邮电出版社, 2017.
GOODFELLOW L, BENGIO Y, COURVILLE A.Deep learning[M]. Beijing: Posts & Telecom Press, 2017.
[19] 张钰, 刘建伟, 左信. 多任务学习[J]. 计算机学报,2020, 43(7): 1340-1378.
ZHANG Y, LIU J W, ZUO X.Survey of multi-task learning[J]. Chinese journal of computers, 2020, 43(7): 1340-1378.
基金
浙江省基础公益技术研究计划（LGF21E070001）; 国家重点研发计划（2017YFA0700300）
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