电能数据集"BanE-16":分析峰值能源需求与环境变量,用于机器学习预测
Imrus Salehin1,2, S M Noman3,4, Mohammad Mahedy Hasan3,5
1Department of Computer Science and Engineering, Daffodil International University, Dhaka 1216, Bangladesh.
Data in brief
|January 18, 2024
概括
BanE-16数据集将电网数据与天气因素联系起来,以改善能源预报. 该资源有助于开发用于可持续能源管理的先进机器学习模型.
科学领域:
- 能源科学 能源科学
- 气象学 天气学
- 数据科学数据科学数据科学
背景情况:
- 准确的能源需求预测对于电网稳定性和管理至关重要.
- 了解气象变量与能源消耗之间的相关性是复杂的.
- 现有的数据集可能缺乏全面整合电网动态和天气数据.
研究的目的:
- 引入Bane-16数据集,这是基于机器学习的能源预测的新型资源.
- 方便分析气候与能源的相关性及其对电力需求的影响.
- 支持用于能源管理和基础设施优化先进预测模型的开发.
主要方法:
- BanE-16数据集将电网动态 (高峰需求,发电统计) 与气象变量 (温度,风速,大气压) 整合在一起.
- 数据集的多变量性质被设计用于复杂的机器学习模型开发.
- 分析侧重于探索复杂的依赖关系和天气与能源的相互关系.
主要成果:
- 该数据集使得天气与能源相关性的复杂分析成为可能.
- 它有助于制定精确的能源预测和基础设施优化.
- 它使研究人员能够深入研究可持续能源规划的细微相互关系.
结论:
- BanE-16数据集是推动能源管理中的预测分析的宝贵资源.
- 它支持创新方法,在动态的能源环境中提供知情决策.
- 利用此数据集可以导致更可持续的能源规划和优化电网运营.
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