基于天空图像的短期光伏发电预测。蒂米什瓦拉西部大学的案例研究

Robert Blaga, C. Dughir, Andreea Săbăduş, N. Stefu, M. Paulescu
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引用次数: 0

摘要

摘要本文研究了PV状态模型在光伏发电小时内功率预测中的性能。pv2状态模型将晴空条件下光伏发电系统输出功率的经验模型与太阳和云相对位置的预测模型联系起来。日照数(SSN)是显示太阳是否发光的二进制量词,用于测量太阳相对于云的位置。一种基于物理的小时内预报方法,处理来自全天成像仪的云场信息,用于预测SSN。SSN预测的质量决定了pv2状态预测的总体质量。pv2状态的性能是根据一个具有挑战性的数据库(天空状态的高变异性、薄云层覆盖、破碎云场、孤立的通过云)进行评估的,该数据库包括辐射数据和罗马尼亚西蒂米什瓦拉大学太阳平台收集的天空图像。从两个角度进行了调查:一般模型的准确性和作为一种新颖的,在天空状态中识别错误SSN预测的特征元素。该分析结果为进一步提高光伏发电功率预测性能的研究奠定了基础。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Short-Term PV Power Forecasting Based on Sky Imagery. A Case Study at the West University of Timisoara
Abstract This study deals with the performance of PV2-state model in intra-hour forecasting of photovoltaic (PV) power. The PV2-state model links an empirical model for estimating the PV power delivered by a PV system under clear-sky with a model for forecasting the relative position of the Sun and clouds. Sunshine number (SSN), a binary quantifier showing if the Sun shines or not, is used as a measure for the Sun position with respect to clouds. A physics-based approach to intra-hour forecasting, processing cloud field information from an all-sky imager, is applied to predict SSN. The quality of SSN prediction conditions the overall quality of PV2-state forecasts. The PV2-state performance was evaluated against a challenging database (high variability in the state-of-the-sky, thin cloud cover, broken cloud field, isolated passing clouds) comprising radiometric data and sky-images collected on the Solar Platform of the West University of Timisoara, Romania. The investigation was performed from two perspectives: general model accuracy and, as a novelty, identification of characteristic elements in the state-of-the-sky which fault the SSN prediction. The outcome of such analysis represents the basis of further research aiming to increase the performance in PV power forecasting.
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