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【24h】 Short-Term Solar Power Forecasting Based on Weighted Gaussian Process Regression

机译基于加权高斯过程回归的短期太阳能发电预测

【摘要】Photovoltaic (PV) power is volatile in nature and raises the level of uncertainty in power systems. PV power forecasting is an important measure to solve this problem. It helps to improve the reliability and reduces the generation cost. Advances in computer technology and sensors make the numeric modeling methods a hotspot in the field of PV power forecasting. However, data modeling methods strongly rely on the accuracy of measurement data. Unavoidable outliers in the measured meteorological data have an adverse effect on the model due to their heteroscedasticity. Although many studies can be found focusing on outlier detection, only a few have incorporated outlier detection with regression models. In this study, an innovative method employing the weighted Gaussian process regression approach is proposed, such that data samples with higher outlier potential have a low weight. A density-based local outlier detection approach is introduced to compensate the deterioration of Euclidean distance for high-dimensional data. A novel concept of the degree of nonlinear correlation is incorporated to compute the contribution of every individual data attribute. Effectiveness of the proposed method is demonstrated by performing an experimental analysis and making comparisons with other typical data-based approaches, and the results exhibit higher estimation accuracy.

【摘要机译】光伏(PV)电源本质上是易挥发的,并提高了电源系统的不确定性水平。光伏发电预测是解决这一问题的重要措施。它有助于提高可靠性并降低发电成本。计算机技术和传感器的进步使数值建模方法成为光伏发电预测领域的热点。但是,数据建模方法强烈依赖于测量数据的准确性。测得的气象数据中不可避免的离群值由于其异方差性而对模型产生不利影响。尽管可以发现许多研究都集中在离群值检测上,但只有少数研究将离群值检测与回归模型结合在一起。在这项研究中,提出了一种采用加权高斯过程回归方法的创新方法,以使具有较高离群值潜力的数据样本具有较低的权重。引入基于密度的局部离群值检测方法来补偿高维数据的欧几里得距离的恶化。引入了非线性相关程度的新颖概念,以计算每个单独数据属性的贡献。通过进行实验分析并与其他典型的基于数据的方法进行比较,证明了该方法的有效性,并且结果显示出更高的估计精度。

【原文格式】PDF

机译 预测;预测模型;计算模型;概率逻辑;数据模型;大气测量;高斯过程;
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