Aguirre-Fraire Baldemar, Beltrán Jessica, Soto-Mendoza Valeria
Centro de Investigación en Matemáticas Aplicadas, Universidad Autonoma de Coahuila, Mexico.
Data Brief. 2024 Apr 18;54:110452. doi: 10.1016/j.dib.2024.110452. eCollection 2024 Jun.
The prediction of domestic electricity consumption is relevant because it helps to plan energy production, among many other benefits. In this work a dataset was collected from one house in an urban city of north-east of Mexico. An ad-hoc acquisition system was implemented to collect the data using a smart meter and the open weather API. The data was collected every minute over a period of 14 months since November 5, 2022, to January 5, 2024. The dataset contains 605,260 samples of 19 variables related with energy consumption and weather data. This dataset is specifically tailored for predicting domestic energy consumption and understanding consumption behaviours, filling a void in the existing literature where such datasets for Mexico are scarce. Moreover, the multivariate nature of the dataset allows researchers to investigate and propose new techniques for forecasting or pattern classification using multivariate data collected in a real scenario.
预测家庭用电量具有重要意义，因为它有助于规划能源生产，还有许多其他益处。在这项工作中，从墨西哥东北部一个城市的一所房屋收集了一个数据集。采用了一个专门的采集系统，使用智能电表和开放天气应用程序编程接口（API）来收集数据。自2022年11月5日至2024年1月5日的14个月期间，每分钟收集一次数据。该数据集包含与能源消耗和天气数据相关的19个变量的605,260个样本。这个数据集是专门为预测家庭能源消耗和理解消费行为而定制的，填补了现有文献中墨西哥此类数据集稀缺的空白。此外，该数据集的多变量性质使研究人员能够利用在实际场景中收集的多变量数据，研究并提出用于预测或模式分类的新技术。
