With the widespread adoption of renewable energy sources in the smart grid era, there is an utmost requirement to develop prediction models that can accurately forecast solar irradiance. The stochastic nature of solar irradiance considerably affects photo-voltaic (PV) power generation. Since weather conditions have a high impact on solar irradiance; therefore, we need weather-conscious forecasting models to boost predictive accuracy. Although Recurrent Neural Networks (RNNs) has shown considerable performance in time-series forecasting problems, its sequential nature prohibits parallelized computing. Recently, architectures based on self-attention mechanism have shown remarkable success in natural language programming (NLP), while being computationally superior. In this paper, we propose an RSAM (Robust Self-Attention Multi-horizon) forecasting architecture, which mainly works in two parts: First, multi-horizon forecasting of solar irradiance using multiple weather parameters; Second, prediction interval analysis for model robustness using quantile regression. A self-attention based Transformer model belonging to the family of deep learning models has been utilized for multi-variate solar time-series forecasting. Using the National Renewable Energy Laboratory (NREL) benchmark datasets of two different sites, we demonstrate that the proposed approach exhibit enhanced performance in comparison to RNN models in terms of RMSE, MAE, MBE, and Forecast skill at each forecasted interval.

中文翻译：

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RSAM：基于稳健自关注的多角度太阳辐照度预测模型

随着智能电网时代可再生能源的广泛采用，迫切需要开发能够准确预测太阳辐照度的预测模型。太阳辐射的随机性在很大程度上影响了光伏（PV）的发电。由于天气状况对太阳辐射有很大影响；因此，我们需要有天气意识的预测模型来提高预测准确性。尽管递归神经网络（RNN）在时间序列预测问题上已显示出可观的性能，但其顺序性质禁止并行计算。最近，基于自我关注机制的体系结构在自然语言编程（NLP）中显示出了惊人的成功，同时在计算上也更胜一筹。在本文中，我们提出了一种RSAM（鲁棒自注意多视点）预测架构，该架构主要工作在两个部分：首先，使用多个天气参数对太阳辐照度进行多视点预测。其次，使用分位数回归对模型稳健性进行预测间隔分析。属于深度学习模型族的基于自注意的Transformer模型已用于多变量太阳时间序列预测。使用两个不同地点的国家可再生能源实验室（NREL）基准数据集，我们证明，与RNN模型相比，该方法在每个预测间隔的RMSE，MAE，MBE和预测技能方面均表现出比RNN模型更高的性能。使用分位数回归的模型稳健性的预测区间分析。属于深度学习模型族的基于自注意的Transformer模型已用于多变量太阳时间序列预测。使用两个不同地点的国家可再生能源实验室（NREL）基准数据集，我们证明，与RNN模型相比，该方法在每个预测间隔的RMSE，MAE，MBE和预测技能方面均表现出比RNN模型更高的性能。使用分位数回归的模型稳健性的预测区间分析。属于深度学习模型族的基于自注意的Transformer模型已用于多变量太阳时间序列预测。使用两个不同地点的国家可再生能源实验室（NREL）基准数据集，我们证明，与RNN模型相比，该方法在每个预测间隔的RMSE，MAE，MBE和预测技能方面均表现出比RNN模型更高的性能。