基于人工情感LSTM算法的智慧家庭能量管理

doi:10.3969/j.issn.2097-0706.2023.11.004

摘要/Abstract

摘要：

随着中国社会城镇化的进一步深入和城市的进一步发展，电力资源逐步出现紧缺情况。不少地区在用电高峰期间由于城市用电超负荷而不得不进行区域性的拉闸限电以缓解城市供电的压力。在这种情况下，用户自主性地管理和节约电能就显得尤为重要。为了使家庭用户可以自主管理电能的使用，实现电网与用户之间的双向互动，同时保证需求侧响应（DR）的执行，提出一种基于人工情感长短期记忆（AELSTM）网络算法的智慧家庭能量管理的控制方法。该方法由人工情感深度神经网络（AEDNN）和长短期记忆（LSTM）网络构成。结合这2部分可以做到人性化实时监测和管理家庭用电情况。

关键词: 人工情感, Q学习算法, 情感深度神经网络, 长短期记忆网络, 能量预测, 智慧家庭能量管理, 智能电网, 低碳经济

Abstract:

With the further deepening of China's social urbanization and the development of cities，there are shortages of social resources，especially power resources. Overload of electricity consumption forces power rationing in some districts to ease the pressure of municipal power supply. In this context，it is particularly important for users to manage and save energy autonomously. In order to enable users of household electricity to independently manage the usage of electric energy ， realize two-way interaction between the power grid and user ends，and ensure the implementation of demand-side response， a smart home energy management method based on artificial emotion long short-term memory （AELSTM）network algorithm is proposed. This method is mainly composed of artificial emotional deep neural network（AEDNN）and long-short-term memory（LSTM）network. The combination of the two components allows humanized real-time monitoring and management of household electricity consumption.

Key words: artificial emotion, Q learning, emotional deep neural network, long short-term memory network, energy forecasting, smart home energy management, intelligent grid, low carbon economy

中图分类号:

TK01⁺8:TP399

殷林飞, 刘金元. 基于人工情感LSTM算法的智慧家庭能量管理[J]. 综合智慧能源, 2023, 45(11): 27-35.

YIN Linfei, LIU Jinyuan. Smart home energy management based on artificial emotion LSTM algorithm[J]. Integrated Intelligent Energy, 2023, 45(11): 27-35.

图/表 14

图1

图2

图3

图4

图5

图6

图7

图8

图9

图10

图11

图12

图13

图14

参考文献 20

[1]	武汉大学国家发展战略研究院课题组. 中国实施绿色低碳转型和实现碳中和目标的路径选择[J]. 中国软科学, 2022(10):1-12.
	Group of National Institute of Development Strategy at Wuhan University. Path choice for China to implement green low-carbon transformation and achieve carbon neutrality[J]. China Soft Science, 2022(10):1-12.
[2]	王琪凯, 熊永康, 陈瑛, 等. 基于Attention机制优化CNN-seq2seq模型的非侵入式负荷监测[J]. 电力系统及其自动化学报, 2022, 34(12):27-34,42.
	WANG Qikai, XIONG Yongkang, CHEN Ying, et al. Non-intrusive load monitoring based on CNN-seq2seq model optimized by attention mechanism[J]. Proceedings of the CSU-EPSA, 2022, 34(12):27-34,42.
[3]	刘东, 裴锡凯, 赖金山, 等. 融合边缘智能计算和联邦学习的隐私保护方案[J]. 电子科技大学学报, 2023, 52(1):95-101.
	LIU Dong, PEI Xikai, LAI Jinshan, et al. Privacy protection scheme combining edge intelligent computing and federated learning[J]. Journal of University of Electronic Science and Technology of China, 2023, 52(1):95-101.
[4]	苏永新, 吴泽旋, 谭貌, 等. 基于深度强化学习的家庭综合需求响应在线优化[J]. 中国电机工程学报, 2021, 41(16):5581-5592.
	SU Yongxin, WU Zexuan, TAN Mao, et al. Online optimization for home integrated demand response based on deep reinforcement learning[J]. Proceedings of the CSEE, 2021, 41(16):5581-5592.
[5]	张耀中, 胡小方, 周跃, 等. 基于多层忆阻脉冲神经网络的强化学习及应用[J]. 自动化学报, 2019, 45(8):1536-1547.
	ZHANG Yaozhong, HU Xiaofang, ZHOU Yue, et al. A novel reinforcement learning algorithm based on multilayer memristive spiking neural network with applications[J]. Acta Automatica Sinica, 2019, 45(8):1536-1547.
[6]	季颖, 王建辉. 基于深度强化学习的微电网在线优化调度[J]. 控制与决策, 2022, 37(7):1675-1684.
	JI Ying, WANG Jianhui. Online optimal scheduling of a microgrid based on deep reinforcement learning[J]. Control and Decision, 2022, 37(7):1675-1684.
[7]	李耀华, 赵承辉, 周逸凡, 等. 基于数据驱动的永磁同步电机深度神经网络控制[J]. 电机与控制学报, 2022, 26(1):115-125.
	LI Yaohua, ZHAO Chenghui, ZHOU Yifan, et al. Deep neural network control for PMSM based on data drive[J]. Electric Machines and Control, 2022, 26(1):115-125.
[8]	俞文帅, 张晓华, 卫志农, 等. 基于深度神经网络的电力系统快速状态估计[J]. 电网技术, 2021, 45(7):2551-2561.
	YU Wenshuai, ZHANG Xiaohua, WEI Zhinong, et al. Fast state estimation for power system based on deep neural network[J]. Power System Technology, 2021, 45(7):2551-2561.
[9]	杨国清, 张凯, 王德意, 等. 基于包络线聚类的多模融合超短期光伏功率预测算法[J]. 电力自动化设备, 2021, 41(2):39-46.
	YANG Guoqing, ZHANG Kai, WANG Deyi, et al. Multi-mode fusion ultra-short-term photovoltaic power prediction algorithm based on envelope clustering[J]. Electric Power Automation Equipment, 2021, 41(2):39-46.
[10]	王传旭, 薛豪. 基于GFU和分层LSTM的组群行为识别研究方法[J]. 电子学报, 2020, 48(8):1465-1471. doi: 10.3969/j.issn.0372-2112.2020.08.002
	WANG Chuanxu, XUE Hao. Group activity recognition based on GFU and hierarchical LSTM[J]. Acta Electronica Sinica, 2020, 48(8):1465-1471. doi: 10.3969/j.issn.0372-2112.2020.08.002
[11]	符杨, 任子旭, 魏书荣, 等. 基于改进LSTM-TCN模型的海上风电超短期功率预测[J]. 中国电机工程学报, 2022, 42(12):4292-4303.
	FU Yang, REN Zixu, WEI Shurong, et al. Ultra-short-term power prediction of offshore wind power based on improved LSTM-TCN model[J]. Proceedings of the CSEE, 2022, 42(12):4292-4303.
[12]	王晨阳, 段倩倩, 周凯, 等. 基于遗传算法优化卷积长短记忆混合神经网络模型的光伏发电功率预测[J]. 物理学报, 2020, 69(10):143-149.
	WANG Chenyang, DUAN Qianqian, ZHOU Kai, et al. A hybrid model for photovoltaic power prediction of both convolutional and long short-term memory neural networks optimized by genetic algorithm[J]. Acta Physica Sinica, 2020, 69(10):143-149.
[13]	史凯钰, 张东霞, 韩肖清, 等. 基于LSTM与迁移学习的光伏发电功率预测数字孪生模型[J]. 电网技术, 2022, 46(4):1363-1372.
	SHI Kaiyu, ZHANG Dongxia, HAN Xiaoqing, et al. Digital twin model of photovoltaic power generation prediction based on LSTM and transfer learning[J]. Power System Technology, 2022, 46(4):1363-1372.
[14]	刘奇, 于斌, 孟祥成, 等. 基于转置卷积神经网络的路面裂缝识别算法[J]. 华南理工大学学报(自然科学版), 2021, 49(12):124-132.
	LIU Qi, YU Bin, MENG Xiangcheng, et al. Pavement crack recognition algorithm based on transposed convolutional neural network[J]. Journal of South China University of Technology(Natural Science Edition), 2021, 49(12):124-132.
[15]	吕政权, 李朝阳, 王海峰, 等. 基于GRU-CNN的综合能源网络安全攻击检测方法[J]. 华电技术, 2021, 43(2): 9-14.
	LYU Zhengquan, LI Zhaoyang, WANG Haifeng, et al. An intrusion detection method for integrated energy network based on GRU-CNN[J]. Huadian Technology, 2021, 43(2): 9-14.
[16]	朱海涛, 林伯韬, 石兰香, 等. 基于Adam优化算法的水平井流动剖面测温反演方法[J]. 中国石油大学学报(自然科学版), 2023, 47(2):99-107.
	ZHU Haitao, LIN Botao, SHI Lanxiang, et al. An inversion method to calculate horizontal well flow profile using temperature data based on Adam optimization algorithm[J]. Journal of China University of Petroleum(Edition of Natural Science), 2023, 47(2):99-107.
[17]	王涛春, 邱庆, 王成田, 等. LD-identify:基于无源RFID的网络学习状态识别[J/OL]. 控制与决策:1-8(2022-09-28) [2023-05-20] https://doi.org/10.13195/j.kzyjc.2022.0622.
	WANG Taochun, QIU Qing, WANG Chengtian, et al. LD-identify:Network learning state recognition based on passive RFID[J/OL]. Control and Decision:1-8(2022-09-28)[2023-05-20]. https://doi.org/10.13195/j.kzyjc.2022.0622.
[18]	雷江龙, 余娟, 向明旭, 等. 基于深度神经网络的数据驱动潮流计算异常误差改进策略[J]. 电力系统自动化, 2022, 46(1):76-84.
	LEI Jianglong, YU Juan, XIANG Mingxu, et al. Improvement strategy for abnormal error of data-driven power flow calculation based on deep neural network[J]. Automation of Electric Power Systems, 2022, 46(1):76-84.
[19]	杨明明. 基于卷积神经网络的机舱风速修正[J]. 华电技术, 2021, 43(5): 75-79.
	YANG Mingming. Wind speed correction for wind turbine based on convolutional neural network[J]. Huadian Technology, 2021, 43(5): 75-79.
[20]	郑真, 朱峰, 马小丽, 等. 基于TL-LSTM的新能源功率短期预测[J]. 综合智慧能源, 2023, 45(1): 41-48. doi: 10.3969/j.issn.2097-0706.2023.01.005
	ZHENG Zhen, ZHU Feng, MA Xiaoli, et al. Short-term new energy power prediction based on TL-LSTM[J]. Integrated Intelligent Energy, 2023, 45(1): 41-48. doi: 10.3969/j.issn.2097-0706.2023.01.005