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埃威尔德:极端天气事件中的大型工业和商业负载数据集
Guolong Liu1,2, Jinjie Liu1, Yan Bai1
1School of Science and Engineering, The Chinese University of Hong Kong, Shenzhen, 518172, China.
这项研究发布了一个大规模的数据集,用于在极端天气期间预测功率负载. 这些数据有助于研究预测由台风和热浪等事件引起的电力需求波动.
科学领域:
- 电力系统工程 电力系统工程
- 数据科学数据科学数据科学
- 气候科学 气候科学
背景情况:
- 准确的负载预测对于电力系统的稳定性和经济运行至关重要.
- 极端天气事件显著扰乱了典型的电力消费模式.
- 缺乏公共数据集阻碍了在极端天气条件下负载预测研究.
研究的目的:
- 为了解决极端天气负载预测中的数据缺口.
- 引入极端天气事件期间电力消耗的综合数据集.
- 促进弹性电网管理的研究和开发.
主要方法:
- 从智能电表收集15分钟间隔的电力消耗数据.
- 收集了来自中国南部386个工业和商业用户的六年来数据.
- 有关极端天气事件 (台风,极端高温) 的5741个特定记录的注释数据.
主要成果:
- 一个新的,大规模的数据集,包括超过5000万的电力消耗记录.
- 涵盖17个不同行业的详细数据.
- 标记记录专门识别极端天气影响的时期.
结论:
- 发布的数据集为推进负载预测模型提供了宝贵的资源.
- 能够更好地理解和预测极端天气期间的电力需求.
- 支持开发更强大,更可靠的电力系统.
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