A moment in the sun: solar nowcasting from multispectral satellite data using self-supervised learning
A moment in the sun: solar nowcasting from multispectral satellite data using self-supervised learning
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DOI：
10.1145/3538637.3538854
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发表时间：
2021-12
期刊：
影响因子：
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通讯作者：
A. S. Bansal;Trapit Bansal;David E. Irwin
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文献类型：
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作者：
A. S. Bansal;Trapit Bansal;David E. Irwin
太阳能现在是历史上最便宜的电力形式。不幸的是，由于太阳能的可变性，大幅增加电网中太阳能的比例仍然具有挑战性，这使得平衡电力的供应和需求变得更加困难。虽然热发电机的斜坡率-他们可以改变他们的能量产生的最大速率-是有限的，太阳能的斜坡率基本上是无限的。因此，准确的近期太阳能预测或即时预报对于提供提前警报以调整热发电机输出以应对太阳能发电的变化以确保供需平衡非常重要。为了解决这个问题，本文开发了一个通用模型，太阳临近预报丰富和现成的多光谱卫星数据，使用自监督学习。具体来说，我们使用卷积神经网络（CNN）和长短期记忆网络（LSTM）开发深度自回归模型，这些模型在多个位置进行全局训练，以预测最近发射的GOES-R系列卫星收集的时空光谱数据的原始未来观测结果。我们的模型基于卫星观测来估计一个位置的近期未来太阳辐照度，我们将其输入到一个回归模型中，该模型是在较小的特定地点太阳能数据上训练的，以提供考虑特定地点特征的近期太阳能光伏（PV）预测。我们评估了我们的方法在不同的覆盖区域和预测视野在25个太阳能站点，并表明它产生的误差接近模型使用地面实况观测。
Solar energy is now the cheapest form of electricity in history. Unfortunately, significantly increasing the electric grid's fraction of solar energy remains challenging due to its variability, which makes balancing electricity's supply and demand more difficult. While thermal generators' ramp rate---the maximum rate at which they can change their energy generation---is finite, solar energy's ramp rate is essentially infinite. Thus, accurate near-term solar forecasting, or nowcasting, is important to provide advance warnings to adjust thermal generator output in response to variations in solar generation to ensure a balanced supply and demand. To address the problem, this paper develops a general model for solar nowcasting from abundant and readily available multispectral satellite data using self-supervised learning. Specifically, we develop deep auto-regressive models using convolutional neural networks (CNN) and long short-term memory networks (LSTM) that are globally trained across multiple locations to predict raw future observations of the spatio-temporal spectral data collected by the recently launched GOES-R series of satellites. Our model estimates a location's near-term future solar irradiance based on satellite observations, which we feed to a regression model trained on smaller site-specific solar data to provide near-term solar photovoltaic (PV) forecasts that account for site-specific characteristics. We evaluate our approach for different coverage areas and forecast horizons across 25 solar sites and show that it yields errors close to that of a model using ground-truth observations.