Staring at the sun: a physical black-box solar performance model
Staring at the sun: a physical black-box solar performance model
复制标题
凝视太阳：物理黑匣子太阳能性能模型
DOI：
10.1145/3276774.3276782
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
David E. Irwin
中科院分区：
文献类型：
--
作者：
Dong Chen;Joseph Breda;David E. Irwin
随着太阳能产生的总能量的增加，开发准确的太阳能性能模型（根据部署的独特物理特性和天气来估计太阳能输出）变得越来越重要。由于基于站点特定信息手动开发“白色盒”物理模型需要专业知识，因此无法扩展，最近的研究集中在使用训练数据自动学习自定义机器学习（ML）模型的“黑盒”方法上。不幸的是，这种方法需要数月至数年的训练数据，并且通常不包含众所周知的太阳能发电物理模型，这降低了其准确性。为了解决这个问题，我们开发了一种物理黑盒建模方法，利用了许多与现有白盒模型相同的基本属性。我们的方法不是手动确定物理模型参数的值，而是通过找到最适合数据的值来自动校准它们。这种校准需要的数据比训练ML模型少得多（只有2个数据点），因为物理模型已经嵌入了输入参数和太阳能输出之间的复杂关系。在开发我们的方法时，我们从11，205个太阳能站点收集的近3.43亿小时天气和太阳能读数中分离出10种不同天气指标对太阳能输出的影响。我们表明，我们的物理模型准确地描述了天气对所有站点太阳能输出的影响，从而消除了使用天气指标训练自定义ML模型的需要。相反，我们通过应用ML来增强我们的物理模型，以仅学习每个站点所特有的关系，特别是非基于天气的阴影。我们对来自100个站点的太阳能和天气数据进行了评估，并表明它比当前最先进的ML方法具有更高的准确性。
Developing accurate solar performance models, which estimate solar output based on a deployment's unique physical characteristics and weather, is increasingly important as the aggregate energy generated from solar rises. Since manually developing "white box" physical models based on site-specific information requires expert knowledge and thus does not scale, recent research focuses on "black box" approaches that use training data to automatically learn a custom machine learning (ML) model. Unfortunately, this approach requires months-to-years of training data, and often does not incorporate well-known physical models of solar generation, which reduces its accuracy. To address the problem, we develop a physical black-box modeling approach that leverages many of the same fundamental properties as existing white-box models. Rather than manually determining values for physical model parameters, our approach automatically calibrates them by finding values that best fit the data. This calibration requires much less data (as few as 2 datapoints) than training a ML model, as the physical model already embeds the complex relationship between the input parameters and solar output. In developing our approach, we isolate the effects of 10 different weather metrics on solar output from nearly 343 million hourly weather and solar readings, or 78,435 aggregate years, gathered from 11,205 solar sites. We show that our physical model accurately describes weather's effect on solar output at all sites, obviating the need for training custom ML models using weather metrics. Instead, we augment our physical model by applying ML to learn only the relationships that are unique to each site, specifically non-weather-based shading. We evaluate our approach on solar and weather data from 100 sites, and show it yields higher accuracy than current state-of-the-art ML approaches.
气象员：揭露大能源数据中基于天气的隐私威胁
DOI：
10.1109/bigdata.2017.8258032
发表时间：
2017
期刊：
IEEE International Conference on Big Data
影响因子：
--
作者：
Chen, Dong;Irwin, David
通讯作者：
Irwin, David