相关实验视频
基于混沌-LSSVM神经网络的新能源电力需求的多时间维度预测
Yidi Wu1, Wei Wang1, Xiaotian Ma2
1State Grid Hebei Marketing Service Center, Shijiazhuang, 050000, China.
Scientific reports
|December 18, 2025
概括
本研究介绍了一个混乱优化的神经网络模型,用于准确预测新能源电力需求. 混合模型提高了跨多个时间框架和空间维度的预测准确性.
科学领域:
- 人工智能的人工智能
- 能源系统 能源系统
- 数据科学数据科学数据科学
背景情况:
- 准确的新能源电力需求预测对于电网稳定至关重要.
- 现有的模型在较低的预测准确度和捕捉时间动态方面扎.
- 多时空和空间预测带来了重大挑战.
研究的目的:
- 开发一种新的混乱优化的最小方形支持向量机 (LSSVM) 神经网络模型.
- 提高对新能源电力需求的时间变化的准确性和动态捕获.
- 为了能够准确的多时空和空间预测新能源的电力需求.
主要方法:
- 使用边缘计算框架进行数据预处理和清理.
- 应用混沌理论和塔肯斯定理用于相位空间重建和空间特征提取.
- 开发了一种混合模型,将长短期内存 (LSTM) 网络和LSSVM结合起来进行预测.
主要成果:
- 实现了0.355千瓦时的平均绝对误差 (MAE) 和1.32%的短期预测的平均绝对百分比误差 (MAPE).
- 在中期预测中获得的MAE为25.36千瓦时和2.15%的MAE.
- 在动态的多时空和空间电力需求预测中表现出稳健性和准确性.
结论:
- 拟议的混乱优化LSSVM模型显著提高了预测准确性.
- 混合方法有效地捕捉了时间动态和空间相关性.
- 该方法为新能源电力需求预测提供了强大的解决方案.
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