基于时序数据处理的分布式光伏功率预测系统

A Distributed Photovoltaic Power Prediction System Based on Time Series Data Processing

【目的】使用高质量的历史数据预测未来光伏发电功率,对高效利用太阳能可再生能源、补充电网供电能力和推进节能减碳具有重要意义。【方法】由于光伏时序数据质量参差不齐,本文提出了面向光伏时序数据的缺失值与异常值处理算法,并基于此搭建了分布式光伏功率预测系统。【结果】该系统可以有效处理多种光伏时序数据,并集成了不同时间尺度及不同时长的功率预测模型,可以对多个电站展开光伏功率预测。【结论】该系统能够满足智能电网对辖区内光伏电站功率预测的需求。

[Objective]Using high-quality historical data to predict future photovoltaic(PV)power generation is of great significance for the efficient use of renewable solar energy,supplementing the power supply capacity of the Power Grid,and promoting energy conservation and carbon reduction.[Methods]Due to the uneven quality of PV time series data,this paper proposes an algorithm for processing missing values and outliers of PV time series data,based on which a distributed PV power prediction system is built.[Results]The system can effectively process a variety of PV time series data,integrate power prediction models of different durations and time scales,and predict PV power for multiple power stations.[Conclusions]This system can meet the demands of a smart Power Grid for power forecasting of PV power stations within its jurisdiction.