【摘要】 随着可再生能源战略地位的提升，光伏发电系统的智能化运行与优化成为能源领域研究热点。对此，文章重点对基于大数据驱动的光伏系统状态预测与优化进行研究，分析了光伏系统多维数据采集与融合处理机制，利用机器学习与深度学习算法提高预测精度，建立多参数融合的系统健康评估体系，使光伏系统状态可视化成为现实。此外，采用预测结果驱动的智能优化决策体系，革新运维策略和电站智能调度，以形成闭环管理机制。更多还原

【Abstract】 With the improvement of the strategic position of renewable energy, the intelligent operation and optimization of photovoltaic power generation system has become a hot research topic in the field of energy. In this regard, this paper focuses on the study of the big data-driven state prediction and optimization of photovoltaic system, analyzes the multi-dimensional data acquisition and fusion processing mechanism of photovoltaic system, improves the prediction accuracy by using machine learning and deep learning algorithms, and establishes a multi-parameter fusion system health assessment system,so as to make the state visualization of photovoltaic system become a reality. In addition, the intelligent optimization decision-making system driven by prediction results is adopted, and the operation and maintenance strategies and power station intelligent dispatching are innovated, in order to form a closed-loop management mechanism.更多还原