预测使用进化超启发算法的可再生能源和电力消耗
Yang Cao1, Jun Yu2, Rui Zhong3
1Graduate School of Information Science and Technology, Hokkaido University, Sapporo, Japan.
Scientific reports
|January 20, 2025
概括
一个新的自动进化超启发式算法,AE-GAPB,增强了可再生能源发电和电力消耗的时间序列预测. 它通过自适应调整参数来提高优化速度和准确性,优于传统方法.
科学领域:
- 计算智能是一种计算智能.
- 能源系统分析 能源系统分析
- 时间序列预测时间序列预测
背景情况:
- 准确预测可再生能源发电和电力消耗对于电网稳定性和管理至关重要.
- 时间序列模型的传统优化算法通常面临着由于固定的搜索模式而导致的适应性和稳定性的限制.
- 需要先进的优化技术来提高预测模型在动态能源市场的性能.
研究的目的:
- 引入和评估一种新的自进化的超启发式算法,AE-GAPB,用于优化时间序列预测模型.
- 通过利用自适应优化,提高预测发电和消费的准确性和效率.
- 为了证明AE-GAPB在现实世界能源数据集中的优化算法中的优越性.
主要方法:
- 开发AE-GAPB算法,集成基因算法 (GA) 在高层和粒子群优化 (PSO) 和蝙蝠算法 (BA) 在低层.
- 该GA动态优化PSO和BA的超参数,以预测准确度为指导.
- 基于代时间和适应性值的GA交叉和突变率的适应性演变,以提高适应性.
主要成果:
- AE-GAPB在可再生能源发电和电力消耗预测方面的预测准确度显著提高.
- 与传统方法相比,自进化的方法大大加快了优化过程.
- 在使用日本能源数据集 (北海道,九州,东北) 的六个时间序列模型的验证证实了AE-GAPB的优秀表现.
结论:
- AE-GAPB为优化能源部门复杂的时间序列预测模型提供了强大而适应性的解决方案.
- 提出的超启发式方法有效地解决了传统优化算法的局限性.
- AE-GAPB为提高可再生能源和电力消耗预测的准确性和效率提供了一个有前途的工具.
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