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Reconstruction of the indoor temperature dataset of a house using data driven models for performance evaluation

机译：使用数据驱动模型重建房屋的室内温度数据集以进行性能评估

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摘要

Whenever the long term monitoring of a building is attempted it is likely that specific sensors or the whole monitoring system used may experience long-term failure therefore creating important gaps in one or more variables of special interest. These long gaps may not be addressed using simple linear interpolation. The option of only using the available data for descriptive statistics would produce results that are biased towards the season of measurement. In addition discarding the incomplete data represents a significant waste of time and effort in the research study. A work around to reduce the bias problem is to predict the missing data from other measured variables using machine-learning techniques. Some questions that follow are: How much data is necessary to be able to train a regression model? What is the expected error of such prediction? What is the best model for such a task? This paper addresses the problem of completing a data set for the interior temperatures inside a passive house using different monitored predictors such as exterior temperature, humidity, wind speed, visibility, pressure and electrical energy use inside the building. Two regression models, multiple linear regression and random forest are compared using learning curves for the training and testing sets for visualizing the so-called bias-variance trade off. The learning curves help to answer the question of optimal sample size for training, model selection and expected error. Finally, descriptive statistics such as median, maximum, minimum, and room temperature averages are presented before and after completing the data sets.
机译：每当尝试对建筑物进行长期监视时，所使用的特定传感器或整个监视系统都可能会遭受长期故障，因此会在一个或多个具有特殊意义的变量中造成重大缺口。使用简单的线性插值可能无法解决这些长间隙。仅将可用数据用于描述性统计的选项会产生偏向于测量季节的结果。另外，丢弃不完整的数据在研究中浪费了大量时间和精力。减少偏差问题的一种变通方法是使用机器学习技术，从其他测量变量中预测丢失的数据。接下来是一些问题：训练回归模型需要多少数据？这种预测的预期误差是什么？这项任务的最佳模式是什么？本文探讨了使用不同的监测预测指标（例如建筑物内部的外部温度，湿度，风速，能见度，压力和电能使用情况）来完成被动房屋内部温度数据集的问题。使用学习曲线将两个回归模型（多元线性回归和随机森林）用于训练和测试集进行比较，以可视化所谓的偏差方差折衷。学习曲线有助于回答训练，模型选择和预期误差的最佳样本量的问题。最后，在完成数据集之前和之后，都将提供描述性统计信息，例如中位数，最大值，最小值和室温平均值。

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