从人类流动到可再生能源：大数据分析接近全球多尺度现象
From human mobility to renewable energies: Big data analysis to approach worldwide multiscale phenomena
摘要：
我们解决并讨论了大数据集分析的最新趋势，重点是研究多尺度现象。描述了大数据分析在不同科学领域的应用，并更详细地探讨了多尺度现象的两个具体例子。第一个涉及单个风力涡轮机规模的风力发电，整个风电场的规模以及整个国家的规模。使用开源数据，我们表明，风力发电在所有这三个尺度具有间歇性的特点，与定义适当的战略，稳定的能源生产的影响。第二个例子涉及人类流动的动力学基础，在不同的尺度上呈现不同的特征。为此，我们分析$12$月数据的Eduroam数据库在葡萄牙的大学，并发现，在最小的规模，通常在一组几个相邻的建筑物，平均位移的特征指数是不同的，从一个国家或一个大陆的规模。
英文摘要：
We address and discuss recent trends in the analysis of big data sets, with the emphasis on studying multiscale phenomena. Applications of big data analysis in different scientific fields are described and two particular examples of multiscale phenomena are explored in more detail. The first one deals with wind power production at the scale of single wind turbines, the scale of entire wind farms and also at the scale of a whole country. Using open source data we show that the wind power production has an intermittent character at all those three scales, with implications for defining adequate strategies for stable energy production. The second example concerns the dynamics underlying human mobility, which presents different features at different scales. For that end, we analyze $12$-month data of the Eduroam database within Portuguese universities, and find that, at the smallest scales, typically within a set of a few adjacent buildings, the characteristic exponents of average displacements are different from the ones found at the scale of one country or one continent.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
