Residential Smart Meter Energy Time Series: Active power measurements with 1s reporting rate

The dataset includes active power measurements for a residential dwelling (apartment) located in Bucharest, Romania, collected at 1s second reporting rate over several months.Always-on appliances include the refrigerator and the wireless router. Several other appliances are installed in the residential unit: washing machine, lighting fixtures, electrical iron, vacuum cleaner, various ICT charging devices, and air conditioning (seldom used).We hope that the dataset is useful to energy systems and computational intelligence researchers for time series forecasting, classification and energy disaggregation tasks.For collecting the energy measurement data the Unbundled Smart Meter (USM) concept is used. The USM approach is a systematization where smart meter functionalities are adequately grouped into two separate (unbundled) components: (i) a module for metrological and hard real-time functions, called the Smart Metrology Meter (SMM), which has fixed (frozen) functionality and high security of recorded data (black box-like standard, where data can be lost only after buffer recirculation after known periods, e.g. 3 months or one year) and (ii) a Smart Meter eXtension (SMX) which has high flexibility to accommodate new functionalities, to be deployed during the meter lifetime and to support the future evolution of the smart grid and energy services.The USM concept is described in detail in:M. Sanduleac, L. Pons, G. Fiorentino, R. Pop and M. Albu, "The unbundled smart meter concept in a synchro-SCADA framework," 2016 IEEE International Instrumentation and Measurement Technology Conference Proceedings, 2016, pp. 1-5, doi: 10.1109/I2MTC.2016.7520459.A data analytics approach using this data set for time series data mining using the Matrix Profile technique for feature extraction is presented in:G. Stamatescu, R. Plamanescu, A. -M. Dumitrescu, I. Ciomei and M. Albu, "Multiscale Data Analytics for Residential Active Power Measurements through Time Series Data Mining," 2022 IEEE 7th International Energy Conference (ENERGYCON), 2022, pp. 1-5, doi: 10.1109/ENERGYCON53164.2022.9830170.

本数据集收录了位于罗马尼亚布加勒斯特的一处住宅（公寓）的主动功率测量数据，该数据以每秒1次的报告频率收集于数月之内。始终开启的电器包括冰箱和无线路由器。住宅单元内还安装了其他电器：洗衣机、照明设备、电熨斗、吸尘器、多种信息通信技术充电设备以及空调（使用频率较低）。我们期望该数据集能为能源系统和计算智能研究人员在时间序列预测、分类和能源分解任务中提供助力。在收集能源测量数据时，采用了分拆智能电表（Unbundled Smart Meter，简称USM）的概念。USM方法是一种系统化方案，其中智能电表的功能被适当地分为两个独立的（分拆的）组件：（一）一个用于计量和硬实时功能的模块，称为智能计量电表（Smart Metrology Meter，简称SMM），该模块具有固定（冻结）的功能和高度的数据记录安全性（类似黑盒标准，数据仅能在已知周期后，例如3个月或一年，经过缓冲区循环后才会丢失）；（二）一个智能电表扩展（Smart Meter eXtension，简称SMX），该模块具有高度的灵活性，以适应新的功能，并在电表使用寿命内部署，同时支持智能电网和能源服务的未来发展。USM概念在M. Sanduleac，L. Pons，G. Fiorentino，R. Pop和M. Albu发表的论文《在同步-SCADA框架中的分拆智能电表概念》中得到了详细描述，该论文发表于2016年IEEE国际仪表与测量技术会议论文集，页码1-5，DOI：10.1109/I2MTC.2016.7520459。此外，G. Stamatescu，R. Plamanescu，A. -M. Dumitrescu，I. Ciomei和M. Albu在《通过时间序列数据挖掘进行住宅主动功率测量的多尺度数据分析》一文中，提出了一种使用该数据集进行时间序列数据挖掘的数据分析方法，该方法采用矩阵轮廓技术进行特征提取，该论文发表于2022年IEEE第7届国际能源会议（ENERGYCON），页码1-5，DOI：10.1109/ENERGYCON53164.2022.9830170。