Short-term PV power forecasts based on rank-alignment

doi:10.19678/j.issn.1000-3428.0252649

Abstract

Abstract: Reliable short-term prediction of photovoltaic power generation power is very important for the dispatch and safety of new energy power, and the planning and operation of energy storage systems. However, there is often a time-domain alignment bias between PV power and related meteorological factors, which makes it difficult for the prediction model to learn a stable quantitative relationship between future PV power and historical meteorological factors, which leads to the problem of low accuracy of PV power prediction. In this paper, the delay embedding model is used to describe the quantitative relationship between PV power and historical meteorological factors in the future, and the time-domain alignment bias between PV power and related meteorological factors is often described based on delay parameterization. The simulation and real data experimental results show that the correction of alignment bias can effectively improve the prediction accuracy.

摘要： 可靠的光伏发电功率短期预测对于新能源电力的调度与安全、储能系统的规划与运行至关重要.而光伏功率与相关气象因素之间常存在时域对齐偏差，该偏差使预测模型难以学习到未来光伏功率与历史气象因素之间稳定的数量关系，导致光伏功率预测的低精度问题.本文利用时延嵌入模型描述未来光伏功率与历史相关气象因素之间的数量关系，基于时延参数化描述光伏功率与相关气象因素之间常存在时域对齐偏差，并基于排序秩设计时延估计方法，将光伏功率与相关气象因素之间的时域对齐问题转化为时延估计问题.仿真和真实数据实验结果表明对齐偏差矫正后能够有效提升预测精度.

Wang Xuguang, Liu Wangjie, Jiao Qiantian, Zhang Mi. Short-term PV power forecasts based on rank-alignment[J]. Computer Engineering, doi: 10.19678/j.issn.1000-3428.0252649.

王旭光, 刘旺杰 , 矫千田, 张咪. 基于排序秩对齐的短期光伏功率预测[J]. 计算机工程, doi: 10.19678/j.issn.1000-3428.0252649.

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