[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. |