TCN-QRNN模型用于短期能源消耗预测,准确度提高,计算效率优化
Lesia Mochurad1, Roman Levkovych2
1Lviv Polytechnic National University, Lviv, 79013, Ukraine. lesia.i.mochurad@lpnu.ua.
Scientific reports
|August 5, 2025
概括
一种新的时间卷积网络-准循环神经网络 (TCN-QRNN) 模型提高了能源消耗预测的准确性和效率. 这种先进的方法比传统方法提供了更高的性能,减少了现实世界能源系统的处理时间和计算负载.
科学领域:
- 人工智能的人工智能
- 机器学习 机器学习
- 能源系统 能源系统
背景情况:
- 传统的循环神经网络 (RNN) 在实时能源预测方面面临着计算复杂性的困难.
- 由于目前预测方法的局限性,有效的能源系统管理受到阻碍.
- 越来越多的数据量和复杂性需要先进的预测解决方案.
研究的目的:
- 提出一种结合时间卷积网络 (TCN) 和准循环神经网络 (QRNN) 的新型混合模型,用于能源消耗预测.
- 解决现有预测模型的计算复杂性和实时应用局限性.
- 提高能源消耗预测的准确性和效率.
主要方法:
- 开发了一个混合TCN-QRNN模型,将TCN的长时间序列处理与QRNN的计算效率相结合.
- 对该模型进行了评估,并将其与长期短期记忆 (LSTM) 和门式循环单元 (GRU) 等既有方法进行了对比.
- 使用包括根平均平方误差 (RMSE),平均绝对误差 (MAE) 和平均绝对百分比误差 (MAPE) 在内的指标来评估性能.
主要成果:
- 与LSTM相比,TCN-QRNN模型的准确性提高了40%,与TCN-LSTM相比提高了8%.
- 与现有模型相比,数据处理时间减少了30%.
- 与LSTM和GRU相比,显示的参数数量明显较小,提高了对资源有限环境的适用性.
结论:
- TCN-QRNN模型为准确和高效的能源消耗预测提供了一个有希望的解决方案.
- 该模型的计算需求减少和高精度使其适合于现实世界能源管理.
- 这种混合方法克服了传统RNN在处理复杂的大规模能源数据方面的局限性.
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