A dataset of exogenous variables and historical electricity demand for short-term load forecasting of the national interconnected electric system (SENI) in the Dominican Republic from 2021 to 2024

IF 1.4 Q3 MULTIDISCIPLINARY SCIENCES

Data in Brief Pub Date : 2025-09-16 DOI:10.1016/j.dib.2025.112057

Rafael Orlando Uceta-Acosta , Deyslen Mariano-Hernandez , Yeulis Rivas-Peña , Víctor S. Ocaña-Guevara , Miguel Aybar-Mejía , Máximo A. Domínguez-Garabitos

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引用次数: 0

Abstract

This dataset contains historical records of electricity demand in the Dominican Republic from January 2021 to December 2024, with hourly resolution. It was compiled to support short-term load forecasting of the National Interconnected Electric System (SENI). The dataset includes the total system demand in megawatts (MW), along with a set of exogenous variables commonly used in forecasting models. These variables include weather data retrieved from Open-Meteo (such as temperature and humidity), time-lagged demand features, and calendar-based indicators (e.g., weekends, holidays, month, hour). All data were collected from open sources, including the official website of the electricity market and system operator, the Organismo Coordinador (OC), as well as public meteorological APIs.

The dataset is structured and cleaned to be directly usable for time series modeling applications. It can be reused by researchers, utility planners, and data scientists for benchmarking forecasting models, developing predictive tools, or supporting energy planning tasks in tropical, developing power systems. The data is provided in CSV format.

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基于外生变量和历史电力需求的数据集，用于2021 - 2024年多米尼加共和国国家互联电力系统（SENI）的短期负荷预测

该数据集包含了多米尼加共和国从2021年1月到2024年12月的电力需求历史记录，以小时为分辨率。它的编制是为了支持国家互联电力系统（SENI）的短期负荷预测。该数据集包括以兆瓦（MW）为单位的系统总需求，以及一组通常用于预测模型的外生变量。这些变量包括从Open-Meteo检索的天气数据（如温度和湿度）、时间滞后的需求特征和基于日历的指标（如周末、节假日、月份、小时）。所有数据均从公开来源收集，包括电力市场和系统运营商的官方网站、组织协调员（OC），以及公共气象api。数据集经过结构化和清理，可以直接用于时间序列建模应用程序。它可以被研究人员、公用事业规划者和数据科学家重用，用于基准预测模型、开发预测工具或支持热带、开发电力系统的能源规划任务。数据以CSV格式提供。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Data in Brief MULTIDISCIPLINARY SCIENCES-

CiteScore

3.10

自引率

0.00%

发文量

996

审稿时长

70 days

期刊介绍： Data in Brief provides a way for researchers to easily share and reuse each other''s datasets by publishing data articles that: -Thoroughly describe your data, facilitating reproducibility. -Make your data, which is often buried in supplementary material, easier to find. -Increase traffic towards associated research articles and data, leading to more citations. -Open up doors for new collaborations. Because you never know what data will be useful to someone else, Data in Brief welcomes submissions that describe data from all research areas.