机器学习算法在国有企业决策中的应用

Dženan Šašić, Zerina Mašetić
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引用次数: 0

摘要

波斯尼亚-黑塞哥维那为了成为欧洲联盟的一员需要满足的条件之一是增加可再生能源发电的份额。本文的目的是利用线性回归算法、支持向量机算法和随机森林算法,从机器学习的角度对这一领域做出贡献——更准确地确定对莫斯塔尔地区风速影响最大的参数。得到的模型表明,对莫斯塔尔地区风速影响最大的参数是短期风加速度。该模型易于推广并适用于其他数据集。
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Application of Machine Learning Algorithms in Decision Making of State Enterprises
One of the conditions that Bosnia and Herzegovina needs to meet in order to become a member of the European Union is to increase the share of electricity production from renewable sources. The aim of this paper is to use a Linear Regression algorithm, Support Vector Machine algorithm and Random Forest algorithm to make a contribution in this area from the aspect of machine learning - more precisely to determine the parameters that most affect the wind speed in Mostar region. The obtained model showed that parameter that most affects the wind speed in Mostar are short-term wind accelerations. The model can be easily generalized and applicable to other data sets.
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