Electricity (Individual household electric power consumption Data Set)

在近 4 年的时间里，以一分钟的采样率测量一个家庭的电力消耗。提供不同的电量和一些分计量值。数据集信息：该档案包含 2075259 次测量，这些测量值在 2006 年 12 月至 2010 年 11 月（47 个月）期间在位于 Sceaux（法国巴黎 7 公里）的一所房屋中收集。注：1.(global_active_power*1000/60 - sub_metering_1 - sub_metering_2 - sub_metering_3) 表示未在子计量 1、2 和 3 中测量的电气设备在家庭中每分钟消耗的有功电能（以瓦特小时为单位）。 2.The数据集包含测量中的一些缺失值（近 1.25% 的行）。数据集中存在所有日历时间戳，但对于某些时间戳，测量值缺失：缺失值由两个连续的分号属性分隔符之间的缺失值表示。例如，数据集显示 2007 年 4 月 28 日的缺失值。 属性信息： 1.date：日期格式为 dd/mm/yyyy 2.time：时间格式为 hh:mm:ss 3.global_active_power：家庭全球分钟平均有功功率（千瓦） 4.global_reactive_power：家庭全球分钟平均无功功率（千瓦） 5.voltage：分钟平均电压（伏特） 6.global_intensity：家庭全球分钟平均电流强度（安培） 7. sub_metering_1：电能分计量1号（有功电能瓦时）。它对应于厨房，主要包含洗碗机、烤箱和微波炉（热板不是电动的，而是燃气驱动的）。 8.sub_metering_2：电能分计量2号（有功电能瓦时）。它对应于洗衣房，里面有洗衣机、烘干机、冰箱和灯。 9.sub_metering_3：3号电能分计量（瓦时有功电能）。它对应于电热水器和空调。我们建议使用以下伪 APA 参考格式来引用此存储库：Dua, D. 和 Graff, C. (2019)。 UCI 机器学习存储库 [http://archive.ics.uci.edu/ml]。加利福尼亚州欧文：加利福尼亚大学信息与计算机科学学院。这里还有一个 BiBTeX 引文：@misc{Dua:2019 , author = "Dua, Dheeru and Graff, Casey", year = "2017", title = "{UCI} Machine Learning Repository", url = "http:// /archive.ics.uci.edu/ml”，机构 = “加州大学欧文分校信息与计算机科学学院”}

Over nearly 4 years, household electricity consumption was measured at a 1-minute sampling rate. Multiple power metrics and sub-metering values are provided.

Dataset Information: This archive contains 2,075,259 measurements collected between December 2006 and November 2010 (47 months) at a house located in Sceaux (7 km from Paris, France).

Notes:
1. The expression `(global_active_power * 1000 / 60 - sub_metering_1 - sub_metering_2 - sub_metering_3)` represents the active energy (in watt-hours) consumed per minute by household electrical equipment not measured by sub-meters 1, 2, and 3.
2. The dataset contains some missing values, accounting for approximately 1.25% of all rows. All calendar timestamps are included in the dataset, but some timestamps have missing measurement values. Missing values are denoted by two consecutive semicolon attribute separators with no content between them. For example, the dataset contains missing values on April 28, 2007.

Attribute Information:
1. date: Date formatted as dd/mm/yyyy
2. time: Time formatted as hh:mm:ss
3. global_active_power: Household global minute-averaged active power (kilowatt)
4. global_reactive_power: Household global minute-averaged reactive power (kilowatt)
5. voltage: Minute-averaged voltage (volt)
6. global_intensity: Household global minute-averaged current intensity (ampere)
7. sub_metering_1: Energy sub-metering No. 1 (active energy in watt-hours). It corresponds to the kitchen, which mainly contains a dishwasher, oven, and microwave (the hot plates are gas-powered, not electric).
8. sub_metering_2: Energy sub-metering No. 2 (active energy in watt-hours). It corresponds to the laundry room, which contains a washing machine, dryer, refrigerator, and lights.
9. sub_metering_3: Energy sub-metering No. 3 (active energy in watt-hours). It corresponds to an electric water heater and air conditioning unit.

We recommend using the following pseudo-APA reference format to cite this repository:
Dua, D. and Graff, C. (2019). UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science.

Here is a BiBTeX citation:
@misc{Dua:2019 , author = "Dua, Dheeru and Graff, Casey", year = "2017", title = "{UCI} Machine Learning Repository", url = "http://archive.ics.uci.edu/ml", institution = "University of California, Irvine, School of Information and Computer Science"}

搜集汇总

背景与挑战

背景概述

该数据集记录了法国一个家庭在47个月内的电力消耗数据，采样频率为一分钟，包含全局功率、电压、电流及三个子区域的分计量值，适用于时间序列分析。数据规模较大（约207万条记录），但存在少量缺失值（约1.25%），主要用于单变量/多元时间序列预测和插补任务。