基于深度学习的智能电网光伏发电预测

Zhengshi Wang, Yuyin Li, Anguo Wang, You Wu, T. Han, Yao Ge
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引用次数: 1

摘要

随着光伏发电技术的不断发展,光伏发电的间歇性和随机性问题日益突出。因此,光伏系统的并网将影响电力系统的稳定性和电力调度。若能对光伏发电进行准确预测,将提高光伏系统发电的协调性和系统并网后电网的稳定性。在光伏系统中,影响光伏发电功率的因素很多,有不同的功率预测算法。本文将长短期记忆(LSTM)用于光伏发电系统的发电量预测。LSTM可以学习时间序列数据的相关特征,避免了传统递归神经网络算法存在的数据梯度消失问题。然后将预测结果直接应用于现有的集成光伏发电存储系统。通过实验验证,预测精度可达到98%以上。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Photovoltaic Power Generation Prediction Based on In-Depth Learning for Smart Grid
With the continuous development of photovoltaic power generation technology, the problems of intermittence and randomness of photovoltaic power generation become prominent. Therefore, the connection of the photovoltaic system to the grid will impact the stability of the power system and power dispatching. If the photovoltaic power generation can be accurately predicted, it will improve the coordination of power generation of the photovoltaic system and the stability of the power grid after the system grid connection. In a photovoltaic system, there are many factors affecting photovoltaic power, and there are different algorithms for power prediction. In this paper, long short-term memory (LSTM) is used to predict the power generation of the photovoltaic power system. LSTM can learn the correlation features of the time series data without the problems of data gradient disappearance of the traditional recurrent neural network algorithm. The prediction results are then directly applied to the existing integrated photovoltaic power storage system. Through the experiments, it is verified that the prediction accuracy can reach higher than 98%.
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