Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour

du Plessis A. A.; Strauss J. M.; Rix A. J.

摘要

Photovoltaic (PV) system power supply is characteristically intermittent. Therefore, PV forecasting is crucial for decision makers responsible for electrical grid stability. With forecast models traditionally trained as macro-level solutions, where a single model emulates the entire PV system, there is uncertainty regarding the ability of these macro-level models to capture the low-level power output dynamics of large multi-megawatt PV systems. Instead, an aggregated inverter-level forecasting methodology is proposed to obtain an enhanced forecasting accuracy. These macro-level and inverter-level forecasting methodologies are implemented with state-of-the-art deep learning based Feedforward neural network, Long Short-Term Memory and Gated Recurrent Unit recurrent neural network models. Results are generated for a real-world scenario, with multi-step forecasts delivered 1-6 h ahead for a 75 MW rated PV system. To ensure the scalability of the proposed methodology, a unique inverter-clustering technique is presented, which reduces the effort of optimising multiple low-level forecast models. A heuristic process of systematic hyperparameter optimisation is also proposed, which serves to guide future forecasting practitioners towards unbiased model development. From the deterministic and probabilistic confidence interval evaluations, overall results demonstrate a marginal increase in forecasting accuracy from the proposed aggregated inverter-level forecasts. The best performing macro-level model obtained Mean Absolute Percentage Error (MAPE) values ranging between 1.42%-8.13% for all weather types and forecast horisons. In comparison, the equivalent inverter-level forecasts delivered MAPE values ranging from 1.27%-8.29%. Finally, it is concluded that deep learning based macro-level forecast models have a sufficient ability to capture low-level PV system behaviour.

机译：光伏（PV）系统电源是特性间歇性的。因此，PV预测对于负责电网稳定性的决策者至关重要。对于传统上培训的预测模型作为宏观级解决方案，其中单个模型仿真整个光伏系统，这些宏观级模型捕获大型多功能PV系统的低级功率输出动态的能力存在不确定性。相反，提出了一个聚合的逆变级预测方法，以获得增强的预测精度。这些宏观级和逆变器级预测方法是基于最先进的基于深度学习的前馈神经网络，长短短期内存和门控复发单元经常性神经网络模型。结果是为真实情景而产生的，对于75兆瓦额定光伏系统，多步预报将进入1-6小时。为了确保所提出的方法的可扩展性，提出了一种独特的逆变器聚类技术，这减少了优化多个低级预测模型的努力。还提出了一种系统Quand参数优化的启发式过程，用于指导未来的预测从业人员迈向无偏的模型发展。从确定性和概率置信区间评估中，总体结果表明，从拟议的聚合逆变器级预测中预测预测准确性的边际增加。最好的宏观级模型获得了所有天气类型和预测Horisons的平均绝对百分比误差（MAPE）值1.42％-8.13％。相比之下，等效的逆变器级预测从1.27％-8.29％的Mape值传递了mape值。最后，得出结论，基于深度学习的宏观预测模型具有足够的能力来捕获低级光伏系统行为。

Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour

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