基于注意力深度学习模型提高微电网性能预测
摘要
本研究旨在解决微电网系统的操作挑战，这些挑战由功率振荡引起，最终导致电网不稳定。提出了一种集成策略，利用卷积层和 Gated Recurrent Unit (GRU) 层的优势，旨在有效地从能源数据集中提取时间序列数据，以提高微电网行为预测的精度。此外，采用注意力层来凸显时间序列数据中的关键特征，优化预测过程。该框架基于多层感知机 (MLP) 模型，该模型负责综合负荷预测和识别异常电网行为。 our methodology underwent rigorous evaluation using the Micro-grid Tariff Assessment Tool dataset, with Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and the coefficient of determination (r2-score) serving as the primary metrics. The approach demonstrated exemplary performance, evidenced by a MAE of 0.39, RMSE of 0.28, and an r2-score of 98.89% in load forecasting, along with near-perfect zero state prediction accuracy (approximately 99.9%). Significantly outperforming conventional machine learning models such as support vector regression and random forest regression, our model's streamlined architecture is particularly suitable for real-time applications, thereby facilitating more effective and reliable microgrid management.
引用
@article{arxiv.2407.14984,
title = {Enhancing Microgrid Performance Prediction with Attention-based Deep Learning Models},
author = {Vinod Kumar Maddineni and Naga Babu Koganti and Praveen Damacharla},
journal= {arXiv preprint arXiv:2407.14984},
year = {2024}
}
备注
2024 11th International Conference on Information Technology, Computer, and Electrical Engineering (ICITACEE)