使用LHS与ANFIS结合的电力负载需求预测
Ahmed G Ismail1, Sayed H A Elbanna2, Hassan S Mohamed2
1Sec. of Operation & Control, North Cairo Distribution Company, Ministry of Electricity & Energy, Cairo, Egypt.
PloS one
|June 10, 2025
概括
预测电力负载需求对于能源管理至关重要. 结合拉丁超立方样本 (LHS) 和自适应神经模糊推理系统 (ANFIS) 的混合方法显著提高了预测准确性和稳定性.
科学领域:
- 电气工程 电气工程
- 人工智能的人工智能
- 数据科学数据科学数据科学
背景情况:
- 准确的负载需求预测对于电力系统管理至关重要,特别是在医疗保健等敏感领域.
- 传统的方法难以处理能源消耗数据中的复杂,非线性模式.
研究的目的:
- 通过使用一种新的混合机器学习方法来提高电荷需求的预测准确度.
- 解决现有模型的局限性,例如过度适应和适应多样化的数据.
主要方法:
- 开发了一种混合方法,将拉丁式超立方采样 (LHS) 结合起来,用于分层输入变量采样和自适应神经模糊推理系统 (ANFIS).
- 该方法包括模拟1000次代的能源需求模式,并使用平均平方误差 (MSE) 评估性能.
- 对比分析包括单独的ANFIS和ANFIS与蒙特卡洛 (MC) 方法相结合.
主要成果:
- 与单独的ANFIS和ANFIS-MC相比,ANFIS-LHS模型表现出卓越的预测性能,实现了更高的准确性和稳定性.
- 拟议的方法显示,与独立的ANFIS模型相比,准确度提高了96.42%.
- 纳入了敏感性分析和风险评估,进一步增强了预测能力.
结论:
- ANFIS-LHS混合模型在预测电荷需求方面取得了重大进展.
- 这种方法有效地克服了以前方法的局限性,提供了更可靠的能源管理解决方案.
- 这些发现有助于在电力系统中进行更具适应性和精确的能源预测.
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