基于Transformer的太阳能光伏电池最大功率点时间序列预测
Transformer based time series prediction of the maximum power point for solar photovoltaic cells
AI总结：
本文提出一种基于Transformer的时间序列预测模型，通过多维环境特征和基于时间的特征对太阳能光伏电池的最大功率点跟踪（MPPT）进行建模，在200小时测试数据中实现了99.54%的平均功率效率和99.98%的峰值功率效率。
摘要：
本文提出了一种改进的基于深度学习的太阳能光伏电池最大功率点跟踪（MPPT）方法，该方法考虑了各种基于时间序列的环境输入。通常，基于人工神经网络的MPPT算法使用基础的神经网络架构和输入，无法全面代表环境条件。在本文中，某个地点的环境条件通过一组全面的环境特征来表示。此外，考虑在输入数据中包含基于时间的特征，以在时间上对大气条件内的周期性模式进行建模，从而实现MPPT算法的稳健建模。使用多维时间序列输入特征，将基于Transformer的深度学习架构训练为时间序列预测模型。该模型在一个包含来自50个地点环境天气条件的典型气象年数据点的数据集上进行训练。Transformer模块中的注意力机制使模型能够高效学习数据中的时间模式。所提出的模型在由200连续小时收集的数据组成的测试数据集中，在非零工作电压点上实现了0.47%的平均百分比误差，导致99.54%的平均功率效率和99.98%的峰值功率效率。所提出的模型通过实时仿真进行了验证。所提出的模型在各种大气条件下以稳健、动态和非潜在的方式执行功率点跟踪。
英文摘要：
This paper proposes an improved deep learning based maximum power point tracking (MPPT) in solar photovoltaic cells considering various time series based environmental inputs. Generally, artificial neural network based MPPT algorithms use basic neural network architectures and inputs which do not represent the ambient conditions in a comprehensive manner. In this article, the ambient conditions of a location are represented through a comprehensive set of environmental features. Furthermore, the inclusion of time based features in the input data is considered to model cyclic patterns temporally within the atmospheric conditions leading to robust modeling of the MPPT algorithm. A transformer based deep learning architecture is trained as a time series prediction model using multidimensional time series input features. The model is trained on a dataset containing typical meteorological year data points of ambient weather conditions from 50 locations. The attention mechanism in the transformer modules allows the model to learn temporal patterns in the data efficiently. The proposed model achieves a 0.47% mean average percentage error of prediction on non zero operating voltage points in a test dataset consisting of data collected over a period of 200 consecutive hours resulting in the average power efficiency of 99.54% and peak power efficiency of 99.98%. The proposed model is validated through real time simulations. The proposed model performs power point tracking in a robust, dynamic, and nonlatent manner, over a wide range of atmospheric conditions.
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