基于LSTM的分布式光伏系统数据价值发掘研究
Research on Data Value Mining of Distributed Photovoltaic System Based on LSTM
阚斌 1杜虎 1高武山 1李浩 1马燕红1
作者信息
- 1. 中节能甘肃武威太阳能发电有限公司,甘肃武威 733000
- 折叠
摘要
随着可再生能源在能源结构中占比的不断提高,分布式光伏系统的数据分析与优化显得尤为重要.文章阐述了长短期记忆网络模型的构建,旨在从分布式光伏系统中发掘数据价值,以准确预测光伏发电量及识别系统异常模式.通过试验验证,模型在预测发电量方面展现了高度的准确性,并能有效识别出数据中的异常模式,为光伏系统的优化运行提供了可靠支持.
Abstract
With the increasing proportion of renewable energy in the global energy structure,data analysis and optimization of distributed photovoltaic systems are particularly important.This paper describes the construction of long and short term memory network model,aiming at exploiting data value from distributed photovoltaic system to accurately predict photovoltaic power generation and identify system abnormal patterns.Through experimental verification,the model shows a high degree of accuracy in predicting power generation,and can effectively identify abnormal patterns in the data,providing reliable support for the optimal operation of the photovoltaic system.
关键词
分布式光伏系统/长短期记忆网络/数据价值发掘/异常模式识别Key words
distributed photovoltaic system/long short-term memory network/data value mining/abnormal pattern recognition引用本文复制引用
出版年
2024
NETL
NSTL
万方数据