摘要翻译：
在这里，我们开发了一种以数据为中心的方法，能够分析慢速充电基础设施周围环境的哪些活动、功能和特征影响了慢速充电基础设施消耗的电力分配。为了获得一个基本的洞察力，我们分析了能量消耗的概率分布及其与表征充电事件的指标的关系。我们收集了地理空间数据集，并利用统计方法对数据进行预处理，我们准备了特征，以模拟充电基础设施运行的空间背景。为了提高结果的统计可靠性，我们将bootstrap方法与Lasso方法相结合，将回归与变量选择能力相结合。我们评估了所选回归系数的统计分布。我们确定了与能源消耗相关的最有影响的特征，表明充电基础设施的空间环境影响其利用模式。其中许多特征都与居民的经济繁荣有关。将所述方法应用于特定类别的充电基础设施，能够例如通过所使用的推出策略来区分所选特征。总体而言，本文展示了统计方法在能源数据中的应用，并提供了对潜在影响能源消耗的因素的见解，这些因素可以在开发模型以指导充电基础设施的部署和电网规划时加以利用。
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英文标题：
《Explaining the distribution of energy consumption at slow charging
  infrastructure for electric vehicles from socio-economic data》
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作者：
Milan Straka, Rui Carvalho, Gijs van der Poel, \v{L}ubo\v{s} Buzna
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最新提交年份：
2020
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分类信息：

一级分类：Statistics        统计学
二级分类：Applications        应用程序
分类描述：Biology, Education, Epidemiology, Engineering, Environmental Sciences, Medical, Physical Sciences, Quality Control, Social Sciences
生物学，教育学，流行病学，工程学，环境科学，医学，物理科学，质量控制，社会科学
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一级分类：Computer Science        计算机科学
二级分类：Machine Learning        机器学习
分类描述：Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
关于机器学习研究的所有方面的论文（有监督的，无监督的，强化学习，强盗问题，等等），包括健壮性，解释性，公平性和方法论。对于机器学习方法的应用，CS.LG也是一个合适的主要类别。
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一级分类：Economics        经济学
二级分类：Econometrics        计量经济学
分类描述：Econometric Theory, Micro-Econometrics, Macro-Econometrics, Empirical Content of Economic Relations discovered via New Methods, Methodological Aspects of the Application of Statistical Inference to Economic Data.
计量经济学理论，微观计量经济学，宏观计量经济学，通过新方法发现的经济关系的实证内容，统计推论应用于经济数据的方法论方面。
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一级分类：Mathematics        数学
二级分类：Optimization and Control        优化与控制
分类描述：Operations research, linear programming, control theory, systems theory, optimal control, game theory
运筹学，线性规划，控制论，系统论，最优控制，博弈论
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英文摘要：
  Here, we develop a data-centric approach enabling to analyse which activities, function, and characteristics of the environment surrounding the slow charging infrastructure impact the distribution of the electricity consumed at slow charging infrastructure. To gain a basic insight, we analysed the probabilistic distribution of energy consumption and its relation to indicators characterizing charging events. We collected geospatial datasets and utilizing statistical methods for data pre-processing, we prepared features modelling the spatial context in which the charging infrastructure operates. To enhance the statistical reliability of results, we applied the bootstrap method together with the Lasso method that combines regression with variable selection ability. We evaluate the statistical distributions of the selected regression coefficients. We identified the most influential features correlated with energy consumption, indicating that the spatial context of the charging infrastructure affects its utilization pattern. Many of these features are related to the economic prosperity of residents. Application of the methodology to a specific class of charging infrastructure enables the differentiation of selected features, e.g. by the used rollout strategy. Overall, the paper demonstrates the application of statistical methodologies to energy data and provides insights on factors potentially shaping the energy consumption that could be utilized when developing models to inform charging infrastructure deployment and planning of power grids.
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PDF链接：
https://arxiv.org/pdf/2006.01672