一个优化系统用于预测智能电网中的能源使用情况,使用时变压器和Aquila优化器进行优化
Namdeo Baban Badhe1, Rahul P Neve1, Vijaykumar P Yele1
1Department of Information Technology, Thakur College Engineering and Technology, Mumbai, India.
Frontiers in artificial intelligence
|April 16, 2025
概括
本研究介绍了一种优化系统,用于智能电网能源使用预测,使用时间融合变压器 (TFT) 和Aquila优化器 (AO). 新的AO-TFT模型显著提高了智能电网的预测准确性和效率.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能的人工智能
- 计算科学 计算科学
背景情况:
- 由于可再生能源的整合和智能电表数据,智能电网需要准确的能源消耗预测.
- 现有的预测模型面临的挑战是复杂的时间序列数据和超参数优化.
研究的目的:
- 开发和评估一种优化的系统,用于预测智能电网中的能源使用情况.
- 提高能源消耗预测模型的准确性和效率.
主要方法:
- 时间序列分析的时间融合变压器 (TFT) 的集成.
- 应用Aquila优化器 (AO) 来对TFT模型进行超参数调整.
- 对LSTM和CNN-BiLSTM等传统模型进行比较分析.
主要成果:
- 拟议的AO-TFT模型表现出卓越的准确性,根平均平方误差 (RMSE) 为0.48,平均绝对误差 (MAE) 为0.31.
- 与传统方法相比,AO-TFT模型的计算时间更快.
- 分析确定了影响能源预测的关键因素,包括建筑类型,天气和负载变化.
结论:
- AO-TFT模型为智能电网能源使用预测提供了高度准确和高效的解决方案.
- 优化的超参数调整对于提高深度学习预测模型的性能至关重要.
- 对混合优化和自适应模型的进一步研究可以推进动态电网管理.
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