丹麦住宅电力前期消费者的综合数据集
Rui Yuan1, S Ali Pourmousavi2, Wen L Soong2
1School of Electrical and Mechanical Engineering, The University of Adelaide, Adelaide, Australia. r.yuan@adelaide.edu.au.
Scientific data
|June 8, 2023
概括
本研究介绍了住宅电力前期消费者的合成数据集,这对于在能源生产增加的情况下对电网规划至关重要. 该数据集解决了数据的局限性,使得人们能够更好地了解未来的能源消耗模式.
科学领域:
- 能源系统 能源系统
- 计算社会科学 计算社会科学
- 数据科学数据科学数据科学
背景情况:
- 由于住宅电力前消费者的增加,电网面临着重大挑战.
- 关于潜在消费者行为的有限数据阻碍了电网规划,投资和商业模式开发.
- 隐私问题和电动汽车 (EV) 和家庭自动化等技术的缓慢采用加剧了数据稀缺.
研究的目的:
- 开发一个全面的合成数据集,代表各种住宅前期消费者电力进出口模式.
- 解决对现实数据的关键需求,以支持研究,公用事业和决策者为一个以前消费者为主导的电网做准备.
- 为了促进对未来未来消费者的电力消费的理解和建模.
主要方法:
- 利用了真正的丹麦消费者数据,全球太阳能估计器 (GSEE) 模型用于光伏发电,以及电动汽车充电数据的emobpy包.
- 综合住宅储能系统 (ESS) 运营商模型.
- 采用生成对抗网络 (GAN) 来合成现实的前期消费者电力数据,为五种前期消费者类型创建数据集.
- 使用定性检查,经验统计,信息理论指标和机器学习评估验证合成数据集的质量.
主要成果:
- 成功生成了一个新的合成数据集,捕捉了住宅前消费者的电力进出口动态.
- 该数据集集成了光伏 (PV) 生产,电动汽车 (EV) 充电和储能系统 (ESS) 操作.
- 验证证实了数据集的质量和代表性,用于研究前期消费者的行为.
结论:
- 开发的合成数据集为理解和规划未来的电网提供了宝贵的资源,具有广泛的住宅前期消费者.
- 这种数据驱动的方法减轻了当前的数据限制,使得电网运行,规划和投资策略更加准确.
- 该方法提供了一个可扩展的解决方案,用于为各种前消费者原型生成现实的能源消费数据.
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