基于长短期记忆网络数字孪生体的短期光伏发电预测
摘要：光伏发电功率的预测对电网稳定以及安全地运行有重要意义，提出一种基于长短期记忆网络(long short term memory ，LSTM)数字孪生体的预测模型，通过数字孪生体模型实现光伏发电功率的精准预测。数字孪生体分为物理空间与数据空间，首先根据物理空间得到的气象孪生数据由LSTM算法获取初步的预测功率，同时更新历史气象数据库。然后在气象数据库中找到相似日，对比相似日的预测功率和实际功率，对初步的预测功率进行误差修正，得到最终光伏功率预测值。文中所提的数字孪生体实现了物理实体与数据驱动的连接，同时物理实体可进行自我学习和更新，因此相较于传统的光伏预测结果更为精确，通过仿真算例进一步证实数字孪生体预测的准确性。
Abstract:The prediction of photovoltaic power generation is of great significance to the stability and safe operation of power grid. A digital twin prediction model based on long short term memory (abbr.LSTM) network was proposed, and by means of digital twin technology model the accurate prediction of photovoltaic (abbr. PV) power generation was realized. The digital Twin can be divided into physical space and data space. Firstly, according to the meteorological twin data obtained from the physical space the preliminary predicted power was obtained by LSTM algorithm, meanwhile the historical meteorological database was updated. Secondly, the similar day was found out in meteorological database, and comparing the predicted power on similar days with the actual power the error correction of the preliminary predicted power was conducted to obtain the predicted value of final PV power. Using the proposed digital twin the connection of the physical entity with the data-driven method was realized, simultaneously the self-studying and updating of the physical entity could be carried out. Therefore, compared with traditional PV prediction results the obtained result was more accurate. The accuracy of digital twin prediction is further verified by simulation example.
标题：基于长短期记忆网络数字孪生体的短期光伏发电预测
英文标题：Short-term Photovoltaic Power Generation Prediction Based on LSTM Digital Twins
作者：樊磊, 张倩, 李国丽, 伍骏杰,
关键词：数字孪生技术, 长短期记忆网络, 光伏发电功率, 功率预测,
发表日期：2023-12-04
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