使用长短期内存进行预测优化,用于相对寒冷的气候能源系统中的太阳能光伏和电动汽车集成,并进行区域案例研究
Tao Hai1,2, Ali B M Ali3, Diwakar Agarwal4
1Artificial Intelligence Research Center (AIRC), College of Engineering and Information Technology, Ajman University, P.O.Box:346, Ajman, United Arab Emirates.
Scientific reports
|May 12, 2025
概括
这项研究通过整合太阳能和电动汽车充电来优化寒冷气候中的可再生能源使用. 机器学习准确地预测需求,提高能源效率并减少电网依赖.
科学领域:
- 可持续能源系统 可持续能源系统
- 整合可再生能源的整合
- 电动汽车的电动移动性
背景情况:
- 越来越多地采用可持续能源和电动移动,在整合可再生能源方面面临挑战,特别是在有可变资源可用性的寒冷气候地区.
- 在动态条件下确保一致的能源性能需要创新的解决方案来管理供需.
- 由于不可预测的可再生资源的可用性,寒冷地区对能源可靠性存在独特的复杂性.
研究的目的:
- 探索寒冷气候住宅社区的能源动态,考虑建筑物的能源需求和电动汽车 (EV) 充电需求.
- 评估从光伏 (PV) 系统产生太阳能发电的潜力.
- 使用机器学习方法优化能源供应和需求管理.
主要方法:
- 估计个别建筑物的能源需求和额外的电动汽车充电需求.
- 评估使用光伏系统的太阳能发电潜力.
- 长短期记忆 (LSTM) 机器学习模型用于能源需求预测和管理的应用.
主要成果:
- 寒冷气候地区的供暖需求远远超过冷却需求.
- 太阳能在温暖的月份覆盖了大约32.1%的能源需求,在寒冷的季节需要电网支持.
- 在预测电动汽车充电模式方面,LSTM模型实现了超过93%的准确性,改善了能源需求预测和负载管理.
结论:
- 优化可再生能源的使用对于减少在寒冷气候下对电网的依赖至关重要.
- 有效的生产需求管理策略,在机器学习的帮助下,可以提高整体能源效率.
- 该研究表明,在具有挑战性的气候条件下,管理可再生能源整合的可行方法.
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