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.

翻译：针对短期太阳辐照度预测，传统点预测方法因太阳能的非平稳特性而效用降低。太阳能的可变性导致维持电网可靠运行所需的运行备用容量增加，发电不确定性越高，运行备用需求越大，从而增加运行成本。本研究提出一种统一架构，利用循环神经网络（RNN）和长短期记忆网络（LSTM）实现日内太阳辐照度多时间尺度预测。本文同时构建了将该建模方法扩展至小时内预测框架的体系，形成能够同时预测小时尺度及日内尺度的多时间跨度预测方法。我们开发了端到端的流水线以实现所提架构。通过系统性实施对预测模型性能进行测试与验证，并通过美国各地地理分散站点的案例研究证明该方法的鲁棒性。预测结果表明，基于统一架构的方法在多时间尺度太阳预测中表现优异，与文献中记录的最优分时间尺度独立建模方法相比，均方根预测误差更低。与文献中基于机器学习的最优方法相比，本方法在所有测试站点的平均均方根误差降低71.5%。此外，所提方法支持基于实时输入的多时间跨度预测，在演进的电网中具有显著的工业应用潜力。