考虑物理特征与行为因素的家庭用能特性建模
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摘要
针对常规负荷建模与预测未考虑用户的行为特性,并且需要大量历史数据作为研究基础的问题,提出一种考虑物理特征与行为因素的家庭用能特性建模方法。以家庭能源中心作为研究对象,在介绍总体分析流程的同时,归纳外部需求、内部转换以及终端能源负荷类型;考虑物理特征与行为因素,建立电器设备的用能模型,并提出模型扩展方法;在此基础上,通过非侵入式负荷分解与马尔可夫链相结合的方法分析模拟用户的用能行为。算例分析表明,所提方法具有独立刻画负荷肖像曲线的能力,不再依赖大量数据进行派生驱动。
Aiming at the problem that modelling and prediction of conventional load do not consider user behavior characteristics and need to take massive historical data as the research basic,a modelling method of household energy consumption characteristics with the consideration of physical features and behavior factors is proposed. Taking the household energy center as the research object,the overall analysis flowchart is introduced,meanwhile the external demand,internal conversion and terminal energy load type are induced. Considering the physical features and behavior factors,the energy consumption model of electrical equipment is built and its expansion method is proposed. On this basis,the user energy consumption behavior is simulated with the combination of non-intrusive load decomposition and Markov chain. Case analysis shows that,the proposed method has the ability to depict load portrait curve independently and no longer depends on massive data for derivation drive.
Aiming at the problem that modelling and prediction of conventional load do not consider user behavior characteristics and need to take massive historical data as the research basic,a modelling method of household energy consumption characteristics with the consideration of physical features and behavior factors is proposed. Taking the household energy center as the research object,the overall analysis flowchart is introduced,meanwhile the external demand,internal conversion and terminal energy load type are induced. Considering the physical features and behavior factors,the energy consumption model of electrical equipment is built and its expansion method is proposed. On this basis,the user energy consumption behavior is simulated with the combination of non-intrusive load decomposition and Markov chain. Case analysis shows that,the proposed method has the ability to depict load portrait curve independently and no longer depends on massive data for derivation drive.
引文
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[17]中华人民共和国建设部.城镇燃气设计规范[M].北京:中国建筑工业出版社,2006:14-15.
[18]ELLEGRD K,COOPER M. Complexity in daily life-a 3D-visualization showing activity patterns in their contexts[J]. Electronic International Journal of Time Use,2004,1(1):37-59.
[19]ZHU Zhicheng,WEI Zhiqiang,YIN Bo,et al. A novel approach for event detection in non-intrusive load monitoring[C]∥2017 IEEE Conference on Energy Internet and Energy System Integration(EI2). Beijing,China:IEEE,2017:1-5.
[20]王闯,燕达,丰晓航,等.基于马氏链与事件的室内人员移动模型[J].建筑科学,2015,31(10):188-198.WANG Chuang,YAN Da,FENG Xiaohang,et al. A Markov chain and event based model for building occupant movement process[J].Building Science,2015,31(10):188-198.
[21]NISSAN USA. Specifications of 2016 Nissan LEAF[EB/OL].[2017-09-06]. http:∥www.nissanusa.com/electric-cars/leaf/versions-specs/version.sv.html.
[2]孙娟.中国典型城市住宅能耗调查与分析[D].上海:同济大学,2009.SUN Juan. Investigation and analysis on energy consumption of typical urban residence in China[D]. Shanghai:Tongji University,2009.
[3]何耀耀,许启发,杨善林,等.基于RBF神经网络分位数回归的电力负荷概率密度预测方法[J].中国电机工程学报,2013,33(1):93-98.HE Yaoyao,XU Qifa,YANG Shanlin,et al. A power load probability density forecasting method based on RBF neural network quantile regression[J]. Proceedings of the CSEE,2013,33(1):93-98.
[4]何耀耀,刘瑞,撖奥洋.基于实时电价与支持向量分位数回归的短期电力负荷概率密度预测方法[J].中国电机工程学报,2017,37(3):768-775.HE Yaoyao,LIU Rui,HAN Aoyang. Short-term power load probability density forecasting method based on real time price and support vector quantile regression[J]. Proceedings of the CSEE,2017,37(3):768-775.
[5]YUKSELTAN E,YUCEKAYA A,BILGE A H. Forecasting electricity demand for Turkey:modeling periodic variations and demand segregation[J]. Applied Energy,2017,193:287-296.
[6] COLLOTTA M,PAU G. An innovative approach for forecasting of energy requirements to improve a smart home management system based on BLE[J]. IEEE Transactions on Green Communications&Networking,2017,1(1):112-120.
[7]陈飞翔,胥建群,王晨杨,等.能源互联网系统用户侧冷热负荷预测模型研究[J].中国电机工程学报,2015,35(14):3678-3684.CHEN Feixiang,XU Jianqun,WANG Chenyang,et al. Research on building cooling and heating load prediction model on user's side in energy internet system[J]. Proceedings of the CSEE,2015,35(14):3678-3684.
[8] SUBBIAH R,PAL A,NORDBERG E K,et al. Energy demand model for residential sector:a first principles approach[J]. IEEE Transactions on Sustainable Energy,2017,8(3):1215-1224.
[9]WIDN J,NILSSON A M,WCKELGRD E. A combined Markov-chain and bottom-up approach to modelling of domestic lighting demand[J]. Energy&Buildings,2009,41(10):1001-1012.
[10]张华一,文福拴,张璨,等.计及舒适度的家庭能源中心运行优化模型[J].电力系统自动化,2016,40(20):32-39.ZHANG Huayi,WEN Fushuan,ZHANG Can,et al. Operation optimization model of home energy hubs considering comfort level of customers[J]. Automation of Electric Power Systems,2016,40(20):32-39.
[11]潘毅群,郁丛,龙惟定,等.区域建筑负荷与能耗预测研究综述[J].暖通空调,2015,45(3):33-40.PAN Yiqun,YU Cong,LONG Weiding,et al. Review of district building load and energy consumption prediction[J]. Journal of HV&AC,2015,45(3):33-40.
[12]MURATORI M,ROBERTS M C,SIOSHANSI R,et al. A highly resolved modeling technique to simulate residential power demand[J]. Applied Energy,2013,107:465-473.
[13]FARZAN F,JAFARI M A,GONG J,et al. A multi-scale adaptive model of residential energy demand[J]. Applied Energy,2015,150:258-273.
[14]WIDN J,WCKELGRD E. A high-resolution stochastic model of domestic activity patterns and electricity demand[J]. Applied Energy,2010,87:1880-1892.
[15]WIDN J,LUNDH M,VASSILEVA I,et al. Constructing load profiles for household electricity and hot water from time-use datamodelling approach and validation[J]. Energy&Buildings,2009,41(7):753-768.
[16]葛少云,李吉峰,李腾,等.配电网和城市路网关联网络的综合可靠性分析[J].中国电机工程学报,2016,36(6):1568-1577.GE Shaoyun,LI Jifeng,LI Teng,et al. Integrated analysis on reliability of power distribution network and urban road network[J].Proceedings of the CSEE,2016,36(6):1568-1577.
[17]中华人民共和国建设部.城镇燃气设计规范[M].北京:中国建筑工业出版社,2006:14-15.
[18]ELLEGRD K,COOPER M. Complexity in daily life-a 3D-visualization showing activity patterns in their contexts[J]. Electronic International Journal of Time Use,2004,1(1):37-59.
[19]ZHU Zhicheng,WEI Zhiqiang,YIN Bo,et al. A novel approach for event detection in non-intrusive load monitoring[C]∥2017 IEEE Conference on Energy Internet and Energy System Integration(EI2). Beijing,China:IEEE,2017:1-5.
[20]王闯,燕达,丰晓航,等.基于马氏链与事件的室内人员移动模型[J].建筑科学,2015,31(10):188-198.WANG Chuang,YAN Da,FENG Xiaohang,et al. A Markov chain and event based model for building occupant movement process[J].Building Science,2015,31(10):188-198.
[21]NISSAN USA. Specifications of 2016 Nissan LEAF[EB/OL].[2017-09-06]. http:∥www.nissanusa.com/electric-cars/leaf/versions-specs/version.sv.html.