聊天图的城市能源负载数据集:峰值和非峰值变化洞察力
Arnab Barua1, Shah Nawaz Haider2, Steve Austin2
1Department of Electrical and Electronic Engineering, University of Science and Technology Chittagong, Chattogram 4202, Bangladesh.
Data in brief
|December 1, 2025
概括
这项研究提供了孟加拉国Chattogram电力需求的详细数据集,将电力使用与天气模式相关联. 研究结果强调热特性和特定湿度是预测城市化地区能源需求的关键预测因素.
科学领域:
- 能源科学 能源科学
- 环境科学 环境科学
- 数据科学数据科学数据科学
背景情况:
- 孟加拉国Chattogram的电力需求因城市化和工业增长而迅速增加.
- 准确的电力需求预测对于发展中国家的电网可靠性和能源效率至关重要.
- 现有的数据集往往缺乏细节或与气象因素的相关性.
研究的目的:
- 在Chattogram,从2021年1月到2023年6月,提供每日电力需求的综合数据集.
- 为了将电力需求 (工业,住宅,基本负载) 与各种气象参数相关联.
- 支持机器学习应用程序,以准确预测能源需求和电网管理.
主要方法:
- 收集了白天高峰,晚上高峰和非高峰时间的每日电力需求数据.
- 集成的电力需求数据与来自NASA POWER的气象数据 (温度,湿度,降水) 结合在一起.
- 进行了相关性分析,以确定电力需求的关键预测因素.
- 通过自动收集和清理过程确保数据完整性.
主要成果:
- 确定了热特性 (例如温度) 和特定湿度作为电力需求的强有力的预测因素.
- 发现相对湿度和降水对电力需求的影响较弱.
- 建立了一个强大的数据集,适合机器学习模型开发.
结论:
- 气象和功率负载数据的综合数据集对于了解能源消耗模式非常有价值.
- 该资源有助于研究能源效率,气候适应性和快速城市化地区的电网可靠性.
- 使用这些数据进行准确的预测可以帮助优化能源分配和基础设施规划.
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