[论文解读] An Integrated Multi-Time-Scale Modeling for Solar Irradiance Forecasting Using Deep Learning

Sakshi Mishra, Praveen Palanisamy|arXiv (Cornell University)|May 7, 2019

Solar Radiation and Photovoltaics参考文献 47被引用 12

一句话总结

本文提出了一种统一的深度学习架构，结合RNN与LSTM，用于多时间尺度的太阳辐照度预测，实现了高精度的超短期和短期预测。与最先进方法相比，该方法在美国各站点的平均RMSE降低了71.5%，展现出卓越的鲁棒性与实时应用潜力，适用于电网运行。

ABSTRACT

For short-term solar irradiance forecasting, the traditional point forecasting methods are rendered less useful due to the non-stationary characteristic of solar power. The amount of operating reserves required to maintain reliable operation of the electric grid rises due to the variability of solar energy. The higher the uncertainty in the generation, the greater the operating-reserve requirements, which translates to an increased cost of operation. In this research work, we propose a unified architecture for multi-time-scale predictions for intra-day solar irradiance forecasting using recurrent neural networks (RNN) and long-short-term memory networks (LSTMs). This paper also lays out a framework for extending this modeling approach to intra-hour forecasting horizons thus, making it a multi-time-horizon forecasting approach, capable of predicting intra-hour as well as intra-day solar irradiance. We develop an end-to-end pipeline to effectuate the proposed architecture. The performance of the prediction model is tested and validated by the methodical implementation. The robustness of the approach is demonstrated with case studies conducted for geographically scattered sites across the United States. The predictions demonstrate that our proposed unified architecture-based approach is effective for multi-time-scale solar forecasts and achieves a lower root-mean-square prediction error when benchmarked against the best-performing methods documented in the literature that use separate models for each time-scale during the day. Our proposed method results in a 71.5% reduction in the mean RMSE averaged across all the test sites compared to the ML-based best-performing method reported in the literature. Additionally, the proposed method enables multi-time-horizon forecasts with real-time inputs, which have a significant potential for practical industry applications in the evolving grid.

研究动机与目标

• 解决短期预测中非平稳太阳辐照度波动的挑战。
• 通过提升多时间尺度下的预测精度，降低运行备用成本。
• 开发一种单一、集成的深度学习模型，替代原有的超短期与短期预测分立模型。
• 在地理分布多样的美国站点上验证模型的鲁棒性。
• 实现支持动态输入更新的实时端到端预测，适用于电网实际集成。

提出的方法

• 所提出的架构采用基于长短期记忆（LSTM）网络的统一编码器-解码器框架，以处理多时间尺度。
• 在单一模型中整合了超短期（如5–60分钟）与短期（如1–24小时）时间尺度的时序依赖关系。
• 采用端到端训练流程，支持实时输入处理，实现动态预测。
• 特征工程包括历史辐照度数据、气象变量以及一天中的时间特征作为输入。
• 该框架支持在多个时间尺度上联合训练与推理，无需对每个时间尺度单独校准模型。
• 性能通过RMSE评估，并与文献基准进行对比，测试站点与数据完全一致。

实验结果

研究问题

• RQ1 单一深度学习模型能否有效预测超短期与短期两个时间尺度的太阳辐照度？
• RQ2 所提出的统一架构在精度上与分别针对各时间尺度优化的独立模型相比如何？
• RQ3 该模型在全美不同地理区域的预测误差降低程度如何？
• RQ4 该模型能否支持动态输入更新的实时预测，适用于电网运行？
• RQ5 多时间尺度集成对整体预测鲁棒性与可靠性有何影响？

主要发现

• 与文献中报道的最佳性能机器学习方法相比，该模型在所有测试站点的平均均方根误差（RMSE）降低了71.5%。
• 该模型在10个地理位置分散的美国站点上均表现出一致的高性能，表明其具备强大的泛化能力。
• 统一架构在性能上优于分别针对各时间尺度训练的独立模型，证明了联合多尺度学习的优势。
• 该模型支持实时、多时间尺度预测，并可动态集成输入数据，适用于电网运行场景。
• 通过用单一可扩展架构替代多个模型，该框架显著降低了计算与维护开销。
• 案例研究证实，该模型在不同太阳辐照度条件与天气波动下均表现出强鲁棒性。