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The Solar Forecast Similarity Method: a new method to compute solar radiation forecasts for the next day

机译:太阳能预测相似方法:一种计算第二天太阳辐射预测的新方法

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The need for PV plant owners to plan what they are injecting in the electricity grid is more and more stringent to avoid endangering the whole supply in electricity. A new solar forecast algorithm, named Solar Forecast Similarity Method, has been developed to predict irradiance for the next day based on a statistical study of the long term HelioClim-3 irradiation database. This algorithm searches in the past for the most similar days compared to the day of interest and uses their following days to produce a forecast. The model has been optimized against the database itself to compute the most adequate set of parameters over France and for the month of January 2014. With this configuration, the results are a null bias and a root mean square error of 48%. The algorithm outperforms the persistence by 20% and the error is similar to existing methods. An objective validation has then been carried out to compare the irradiance forecasts to high quality measurements from several Baseline Surface Radiation Network (BSRN) ground stations. The method is very promising since the comparison results are in line or lower than the one obtained with the first validation analysis performed on the HelioClim-3 database. For high frequencies, however, predictions have a high error for rapidly varying weather. This demonstrates that the method provides information for the averaged production the following day but requires another input to reliably predict high frequency irradiance.
机译:需要PV工厂所有者计划他们在电网中注射的东西是越来越严格的,以避免危及整个电力供应。已经开发了一种新的太阳能预测算法,名为Solar预测相似性方法,以基于长期Helioclim-3辐照数据库的统计研究来预测第二天的辐照度。与兴趣日相比,此算法在过去中搜索最相似的日子,并使用他们的后续天来产生预测。该模型已经针对数据库本身进行了优化,以计算法国和2014年1月份的最适量的参数集合。使用此配置,结果为空偏置,均为48%的均方根误差。该算法优于持久性20%,并且错误类似于现有方法。然后已经进行了客观验证,以将辐照度预测与来自几个基线表面辐射网络(BSRN)地面站的高质量测量进行比较。该方法非常有希望,因为比较结果始于或低于通过在Helioclim-3数据库上执行的第一验证分析获得的那样。然而,对于高频,预测对于快速变化的天气具有很高的错误。这表明该方法提供了前一天的平均生产的信息,但需要另一个输入来可靠地预测高频辐照度。

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