论文标题
增强能源研究中的算法：系统评价
Boosting algorithms in energy research: A systematic review
论文作者
论文摘要
机器学习算法由于其灵活性，自动化和处理大数据的能力，在能源研究中已被广泛利用。最杰出的机器学习算法是增强算法，这些算法被称为“从愚人理事会中获得智慧”，从而将薄弱的学习者转变为强大的学习者。增强算法的特征是高灵活性和高解释性。后一种财产是统计界最近发展的结果。在这项工作中，我们提供了对增强算法的特性的理解，以更好地利用其在能源研究中的优势。在这方面，（a）我们总结了有关增强算法的最新进展，（b）我们回顾了能源研究中的相关应用，这些应用程序专注于可再生能源的人（尤其是那些专注于风能和太阳能的人），其中包括大量总体的能源，以及（c）（c）我们描述了与他们的质量相关的属性以及其属性的相关性。我们表明，到目前为止，增强措施已经没有得到充分利用，而在解释和解释以及预测性能方面，能量领域的巨大进步既可以取得了进步。
Machine learning algorithms have been extensively exploited in energy research, due to their flexibility, automation and ability to handle big data. Among the most prominent machine learning algorithms are the boosting ones, which are known to be "garnering wisdom from a council of fools", thereby transforming weak learners to strong learners. Boosting algorithms are characterized by both high flexibility and high interpretability. The latter property is the result of recent developments by the statistical community. In this work, we provide understanding on the properties of boosting algorithms to facilitate a better exploitation of their strengths in energy research. In this respect, (a) we summarize recent advances on boosting algorithms, (b) we review relevant applications in energy research with those focusing on renewable energy (in particular those focusing on wind energy and solar energy) consisting a significant portion of the total ones, and (c) we describe how boosting algorithms are implemented and how their use is related to their properties. We show that boosting has been underexploited so far, while great advances in the energy field are possible both in terms of explanation and interpretation, and in terms of predictive performance.
