With the recent widespread deployment of Photovoltaic (PV) panels in the northern snow-prone areas, performance analysis of these panels is getting much more importance. Partial or full reduction in energy yield due to snow accumulation on the surface of PV panels, which is referred to as snow loss, reduces their operational efficiency. Developing intelligent algorithms to accurately predict the future snow loss of PV farms is addressed in this article for the first time. The article proposes daily snow loss prediction models using machine learning algorithms solely based on meteorological data. The algorithms include regression trees, gradient boosted trees, random forest, feed-forward and recurrent artificial neural networks, and support vector machines. The prediction models are built based on the snow loss of a PV farm located in Ontario, Canada which is calculated using a 3-stage model and hourly data records over a 4-year period. The stages of the aforementioned model consist of: stage I: yield determination, stage II: power loss calculation, and stage III: snow loss extraction. The functionality of the proposed prediction models is validated over the historical data and the optimal hyperparameters are selected for each model to achieve the best results. Among all the models, gradient boosted trees obtained the minimum prediction error and thus the best performance. The results achieved prove the effectiveness of the proposed models for the prediction of daily snow loss of PV farms.

中文翻译：

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使用计算智能技术的光伏发电场雪损预测

随着最近在北部多雪地区广泛部署光伏 (PV) 面板，对这些面板的性能分析变得越来越重要。由于 PV 电池板表面积雪而导致的能量产量部分或全部减少，称为雪损失，降低了它们的运行效率。本文首次讨论了开发智能算法以准确预测光伏电站未来的降雪量。文章提出了使用仅基于气象数据的机器学习算法的每日降雪量预测模型。这些算法包括回归树、梯度提升树、随机森林、前馈和循环人工神经网络以及支持向量机。预测模型是基于位于安大略省的光伏农场的雪损失建立的，加拿大 这是使用 3 阶段模型和 4 年期间的每小时数据记录计算的。上述模型的阶段包括：阶段 I：产量确定、阶段 II：功率损失计算和阶段 III：雪损失提取。所提出的预测模型的功能在历史数据上得到验证，并为每个模型选择最佳超参数以实现最佳结果。在所有模型中，梯度提升树获得了最小的预测误差，从而获得了最好的性能。所取得的结果证明了所提出的模型在预测光伏发电场每日降雪量方面的有效性。功率损失计算，以及第三阶段：雪损失提取。所提出的预测模型的功能在历史数据上得到验证，并为每个模型选择最佳超参数以实现最佳结果。在所有模型中，梯度提升树获得了最小的预测误差，从而获得了最好的性能。所取得的结果证明了所提出的模型在预测光伏发电场每日降雪量方面的有效性。功率损失计算，以及第三阶段：雪损失提取。所提出的预测模型的功能在历史数据上得到验证，并为每个模型选择最佳超参数以实现最佳结果。在所有模型中，梯度提升树获得了最小的预测误差，从而获得了最好的性能。所取得的结果证明了所提出的模型在预测光伏发电场每日降雪量方面的有效性。