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Resident consumption expenditure forecast based on embedded system and machine learning

机译：基于嵌入式系统和机器学习的居民消费支出预测

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摘要

The relationship between income and expenditure is significant for understanding the shape of the residents' economic dynamics. Consumer spending and household disposable income by the relationship between Machine Learning (ML) programming nonparametric locally weighted scatterplot smoothing regression analyzes. This study aims to determine the relationship between variables directly rather than the traditional parameters of regression. According to the survey, the usual assumptions, income and number of residents increase the impact of a sharp increase in spending at first, then slowed down. This growth can be relatively high mandatory spending little residents to explain. It increases according to income levels in middle-income and high-income groups. With the changes in population size, expenditure changes are limited in middle-income levels, restricted in most high-income levels. The latest growth in the smart meter housing sector is a large data set. Near real-time access to each household's power consumption by both supply and demand can extract valuable information for effective energy management. Predicted consumption will help improve power generation companies and demand-side management programs, but it is not. Consumption of individual households is very irregular, is a trivial task. In the business field of energy load forecasting, machine learning involves a lot of work. Machine learning methods are the so-called black-box model. The internal dynamic is almost unknown. However, without manual intervention, learning the ability to complex internal representation is a significant advantage.
机译：收入和支出之间的关系对于了解居民的经济动态的形状是重要的。消费者支出和家用的一次性收入通过机器学习（ML）之间的关系进行编程非参数局部加权散点图平滑回归分析。本研究旨在直接确定变量之间的关系，而不是传统的回归参数。根据调查，通常的假设，收入和居民人数提高了起初支出急剧增加的影响，然后减速了。这种增长可能是相对较高的强制性花费小居民解释。它根据中等收入和高收入群体的收入水平增加。随着人口规模的变化，支出变化在中等收入水平中受到限制，限制在最高收入水平。智能电表住房扇区的最新增长是大数据集。通过供应和需求靠近每个家庭的功耗近的实时访问，可以提取有效的能源管理信息。预测消费将有助于改善发电公司和需求方管理计划，但它不是。个人家庭的消费是非常不规则的，是一个琐碎的任务。在能量负荷预测的业务领域，机器学习涉及大量的工作。机器学习方法是所谓的黑匣子型号。内部动态几乎未知。但是，如果没有手动干预，学习复杂内部表示的能力是一个显着的优势。

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